Machine Learning Engineer Interview Questions and Answers

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What is Machine Learning?

Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. You select a model to train and then manually perform feature extraction. Used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics.

What is Supervised Learning? 

Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples.

Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision Trees, K-nearest Neighbor Algorithm and Neural Networks

Example: If you built a fruit classifier, the labels will be “this is an orange, this is an apple and this is a banana”, based on showing the classifier examples of apples, oranges and bananas.

What is Unsupervised learning?

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses.

Algorithms: Clustering, Anomaly Detection, Neural Networks and Latent Variable Models

Example: In the same example, a fruit clustering will categorize as “fruits with soft skin and lots of dimples”, “fruits with shiny hard skin” and “elongated yellow fruits”.

Explain the difference between supervised and unsupervised machine learning?

In supervised machine learning algorithms, we have to provide labeled data, for example, prediction of stock market prices, whereas in unsupervised we need not have labeled data, for example, classification of emails into spam and non-spam.

What are the parametric models? Give an example.

Parametric models are those with a finite number of parameters. To predict new data, you only need to know the parameters of the model. Examples include linear regression, logistic regression, and linear SVMs.
Non-parametric models are those with an unbounded number of parameters, allowing for more flexibility. To predict new data, you need to know the parameters of the model and the state of the data that has been observed. Examples include decision trees, k-nearest neighbors, and topic models using latent Dirichlet analysis.

What is the difference between classification and regression?

Classification is used to produce discrete results, classification is used to classify data into some specific categories. For example, classifying emails into spam and non-spam categories.
Whereas, We use regression analysis when we are dealing with continuous data, for example predicting stock prices at a certain point in time.

What Is Overfitting, and How Can You Avoid It?

Overfitting is a situation that occurs when a model learns the training set too well, taking up random fluctuations in the training data as concepts. These impact the model’s ability to generalize and don’t apply to new data.
When a model is given the training data, it shows 100 percent accuracy—technically a slight loss. But, when we use the test data, there may be an error and low efficiency. This condition is known as overfitting.
There are multiple ways of avoiding overfitting, such as:
● Regularization. It involves a cost term for the features involved with the objective function
● Making a simple model. With lesser variables and parameters, the variance can be reduced
● Cross-validation methods like k-folds can also be used
● If some model parameters are likely to cause overfitting, techniques for regularization like LASSO can be used that penalize these parameters

What is meant by ‘Training set’ and ‘Test Set’?

We split the given data set into two different sections namely, ‘Training set’ and ‘Test Set’.
‘Training set’ is the portion of the dataset used to train the model.
‘Testing set’ is the portion of the dataset used to test the trained model.

How Do You Handle Missing or Corrupted Data in a Dataset?

One of the easiest ways to handle missing or corrupted data is to drop those rows or columns or replace them entirely with some other value.
There are two useful methods in Pandas:
● IsNull() and dropna() will help to find the columns/rows with missing data and drop them
● Fillna() will replace the wrong values with a placeholder value

Explain Ensemble learning.

In ensemble learning, many base models like classifiers and regressors are generated and combined together so that they give better results. It is used when we build component classifiers that are accurate and independent. There are sequential as well as parallel ensemble methods.

Explain the Bias-Variance Tradeoff.

Predictive models have a tradeoff between bias (how well the model fits the data) and variance (how much the model changes based on changes in the inputs).
Simpler models are stable (low variance) but they don’t get close to the truth (high bias).
More complex models are more prone to overfitting (high variance) but they are expressive enough to get close to the truth (low bias). The best model for a given problem usually lies somewhere in the middle.

What is the difference between stochastic gradient descent (SGD) and gradient descent (GD)?

Both algorithms are methods for finding a set of parameters that minimize a loss function by evaluating parameters against data and then making adjustments.
In standard gradient descent, you’ll evaluate all training samples for each set of parameters.
This is akin to taking big, slow steps toward the solution.
In stochastic gradient descent, you’ll evaluate only 1 training sample for the set of parameters before updating them. This is akin to taking small, quick steps toward the solution.

How Can You Choose a Classifier Based on a Training Set Data Size?

When the training set is small, a model that has a right bias and low variance seems to work better because they are less likely to overfit. For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work fine with complex relationships. 

What are 3 data preprocessing techniques to handle outliers?

1. Winsorize (cap at threshold).
2. Transform to reduce skew (using Box-Cox or similar).
3. Remove outliers if you’re certain they are anomalies or measurement errors.

How much data should you allocate for your training, validation, and test sets?

You have to find a balance, and there’s no right answer for every problem.
If your test set is too small, you’ll have an unreliable estimation of model performance (performance statistic will have high variance). If your training set is too small, your actual model parameters will have a high variance.
A good rule of thumb is to use an 80/20 train/test split. Then, your train set can be further split into train/validation or into partitions for cross-validation.

What Is a False Positive and False Negative and How Are They Significant?

False positives are those cases which wrongly get classified as True but are False.
False negatives are those cases which wrongly get classified as False but are True.
In the term ‘False Positive’, the word ‘Positive’ refers to the ‘Yes’ row of the predicted value in
the confusion matrix. The complete term indicates that the system has predicted it as a positive, but the actual value is negative.

What’s a Fourier transform?

A Fourier transform is a generic method to decompose generic functions into a superposition of symmetric functions. Or as this more intuitive tutorial puts it, given a smoothie, it’s how we find the recipe. The Fourier transform finds the set of cycle speeds, amplitudes, and phases to match any time signal. A Fourier transform converts a signal from time to frequency domain — it’s a very common way to extract features from audio signals or other time series such as sensor data.

What is deep learning, and how does it contrast with other machine learning algorithms?

Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data. In that sense, deep learning represents an unsupervised learning algorithm that learns representations of data through the use of neural nets.

What’s the difference between a generative and discriminative model?

A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data. Discriminative models will generally outperform generative models on classification tasks.

What Are the Applications of Supervised Machine Learning in Modern Businesses?

Applications of supervised machine learning include:
Email Spam Detection
Here we train the model using historical data that consists of emails categorized as spam or not spam. This labeled information is fed as input to the model.
Healthcare Diagnosis
By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not.
Sentiment Analysis
This refers to the process of using algorithms to mine documents and determine whether they’re positive, neutral, or negative in sentiment.
Fraud Detection
Training the model to identify suspicious patterns, we can detect instances of possible fraud.

What Is Semi-supervised Machine Learning?

Supervised learning uses data that is completely labeled, whereas unsupervised learning uses no training data.
In the case of semi-supervised learning, the training data contains a small amount of labeled data and a large amount of unlabeled data.

What Are Unsupervised Machine Learning Techniques?

There are two techniques used in unsupervised learning: clustering and association.

Clustering
● Clustering problems involve data to be divided into subsets. These subsets, also called clusters, contain data that are similar to each other. Different clusters reveal different details about the objects, unlike classification or regression.

Association
● In an association problem, we identify patterns of associations between different variables or items.
● For example, an eCommerce website can suggest other items for you to buy, based on the prior purchases that you have made, spending habits, items in your wishlist, other customers’ purchase habits, and so on.

Explain Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA) is a common method of topic modeling, or classifying documents by subject matter.
LDA is a generative model that represents documents as a mixture of topics that each have their own probability distribution of possible words.
The “Dirichlet” distribution is simply a distribution of distributions. In LDA, documents are distributions of topics that are distributions of words.

Explain Principle Component Analysis (PCA)

PCA is a method for transforming features in a dataset by combining them into uncorrelated linear combinations.
These new features, or principal components, sequentially maximize the variance represented (i.e. the first principal component has the most variance, the second principal component has the second most, and so on).
As a result, PCA is useful for dimensionality reduction because you can set an arbitrary variance cutoff.

What’s the F1 score? How would you use it?

The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. You would use it in classification tests where true negatives don’t matter much.

When should you use classification over regression?

Classification produces discrete values and dataset to strict categories, while regression gives you continuous results that allow you to better distinguish differences between individual points.
You would use classification over regression if you wanted your results to reflect the belongingness of data points in your dataset to certain explicit categories (ex: If you wanted to know whether a name was male or female rather than just how correlated they were with male and female names.)

How do you ensure you’re not overfitting with a model?

This is a simple restatement of a fundamental problem in machine learning: the possibility of overfitting training data and carrying the noise of that data through to the test set, thereby providing inaccurate generalizations.
There are three main methods to avoid overfitting:
1- Keep the model simpler: reduce variance by taking into account fewer variables and parameters, thereby removing some of the noise in the training data.
2- Use cross-validation techniques such as k-folds cross-validation.
3- Use regularization techniques such as LASSO that penalize certain model parameters if they’re likely to cause overfitting.

How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?

While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines:
● If accuracy is a concern, test different algorithms and cross-validate them
● If the training dataset is small, use models that have low variance and high bias
● If the training dataset is large, use models that have high variance and little bias

How Do You Design an Email Spam Filter?

Building a spam filter involves the following process:

● The email spam filter will be fed with thousands of emails
● Each of these emails already has a label: ‘spam’ or ‘not spam.’
● The supervised machine learning algorithm will then determine which type of emails are being marked as spam based on spam words like the lottery, free offer, no money, full refund, etc.
● The next time an email is about to hit your inbox, the spam filter will use statistical analysis and algorithms like Decision Trees and SVM to determine how likely the email is spam
● If the likelihood is high, it will label it as spam, and the email won’t hit your inbox
● Based on the accuracy of each model, we will use the algorithm with the highest accuracy after testing all the models

Explain bagging.

Bagging, or Bootstrap Aggregating, is an ensemble method in which the dataset is first divided into multiple subsets through resampling.
Then, each subset is used to train a model, and the final predictions are made through voting or averaging the component models.
Bagging is performed in parallel.

What is the ROC Curve and what is AUC (a.k.a. AUROC)?

The ROC (receiver operating characteristic) the performance plot for binary classifiers of True Positive Rate (y-axis) vs. False Positive Rate (xaxis).
AUC is the area under the ROC curve, and it’s a common performance metric for evaluating binary classification models.
It’s equivalent to the expected probability that a uniformly drawn random positive is ranked before a uniformly drawn random negative.

Why is Area Under ROC Curve (AUROC) better than raw accuracy as an out-of-sample evaluation metric?

AUROC is robust to class imbalance, unlike raw accuracy.
For example, if you want to detect a type of cancer that’s prevalent in only 1% of the population, you can build a model that achieves 99% accuracy by simply classifying everyone has cancer-free.

What are the advantages and disadvantages of neural networks?

Advantages: Neural networks (specifically deep NNs) have led to performance breakthroughs for unstructured datasets such as images, audio, and video. Their incredible flexibility allows them to learn patterns that no other ML algorithm can learn.
Disadvantages: However, they require a large amount of training data to converge. It’s also difficult to pick the right architecture, and the internal “hidden” layers are incomprehensible.

Define Precision and Recall.

Precision
● Precision is the ratio of several events you can correctly recall to the total number of events you recall (mix of correct and wrong recalls).
● Precision = (True Positive) / (True Positive + False Positive)
Recall
● A recall is the ratio of a number of events you can recall the number of total events.
● Recall = (True Positive) / (True Positive + False Negative)

What Is Decision Tree Classification?

A decision tree builds classification (or regression) models as a tree structure, with datasets broken up into ever-smaller subsets while developing the decision tree, literally in a tree-like way with branches and nodes. Decision trees can handle both categorical and numerical data.

What Is Pruning in Decision Trees, and How Is It Done?

Pruning is a technique in machine learning that reduces the size of decision trees. It reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.
Pruning can occur in:
● Top-down fashion. It will traverse nodes and trim subtrees starting at the root
● Bottom-up fashion. It will begin at the leaf nodes
There is a popular pruning algorithm called reduced error pruning, in which:
● Starting at the leaves, each node is replaced with its most popular class
● If the prediction accuracy is not affected, the change is kept
● There is an advantage of simplicity and speed

What Is a Recommendation System?

Anyone who has used Spotify or shopped at Amazon will recognize a recommendation system:
It’s an information filtering system that predicts what a user might want to hear or see based on choice patterns provided by the user.

What Is Kernel SVM?

Kernel SVM is the abbreviated version of the kernel support vector machine. Kernel methods are a class of algorithms for pattern analysis, and the most common one is the kernel SVM.

What Are Some Methods of Reducing Dimensionality?

You can reduce dimensionality by combining features with feature engineering, removing collinear features, or using algorithmic dimensionality reduction.
Now that you have gone through these machine learning interview questions, you must have got an idea of your strengths and weaknesses in this domain.

What evaluation approaches would you work to gauge the effectiveness of a machine learning model?

You would first split the dataset into training and test sets, or perhaps use cross-validation techniques to further segment the dataset into composite sets of training and test sets within the data. You should then implement a choice selection of performance metrics: here is a fairly comprehensive list. You could use measures such as the F1 score, the accuracy, and the confusion matrix. What’s important here is to demonstrate that you understand the nuances of how a model is measured and how to choose the right performance measures for the right situations.

What Are the Three Stages of Building a Model in Machine Learning?

The three stages of building a machine learning model are:
● Model Building Choose a suitable algorithm for the model and train it according to the requirement
● Model Testing Check the accuracy of the model through the test data
● Applying the Mode Make the required changes after testing and use the final model for real-time projects. Here, it’s important to remember that once in a while, the model needs to be checked to make sure it’s working correctly. It should be modified to make sure that it is up-to-date.

How is KNN different from k-means clustering?

K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this really means is that in order for K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into (thus the nearest neighbor part). K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.

What are difference between Data Mining and Machine learning?

Machine learning relates to the study, design, and development of the algorithms that give computers the capability to learn without being explicitly programmed. While data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns. During this processing machine, learning algorithms are used.

What are the different Algorithm techniques in Machine Learning?

The different types of techniques in Machine Learning are
● Supervised Learning
● Unsupervised Learning
● Semi-supervised Learning
● Reinforcement Learning
● Transduction
● Learning to Learn

What are the various Machine Learning algorithms?

What is cross-validation?

Reference: k-fold cross validation 

Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation. When a specific value for k is chosen, it may be used in place of k in the reference to the model, such as k=10 becoming 10-fold cross-validation. Mainly used in backgrounds where the objective is forecast, and one wants to estimate how accurately a model will accomplish in practice.

Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.

It is a popular method because it is simple to understand and because it generally results in a less biased or less optimistic estimate of the model skill than other methods, such as a simple train/test split.

The general procedure is as follows:
1. Shuffle the dataset randomly.
2. Split the dataset into k groups
3. For each unique group:
a. Take the group as a hold out or test data set
b. Take the remaining groups as a training data set
c. Fit a model on the training set and evaluate it on the test set
d. Retain the evaluation score and discard the model
4. Summarize the skill of the model using the sample of model evaluation scores

What is “Naive” in a Naive Bayes?

Reference: Naive Bayes Classifier on Wikipedia

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable. Bayes’ theorem states the following relationship, given class variable y and dependent feature vector X1through Xn:

Machine Learning Algorithms Naive Bayes

What is PCA (Principal Component Analysis)? When do you use it?

Reference: PCA on wikipedia

Principal component analysis (PCA) is a statistical method used in Machine Learning. It consists in projecting data in a higher dimensional space into a lower dimensional space by maximizing the variance of each dimension.

The process works as following. We define a matrix A with > rows (the single observations of a dataset – in a tabular format, each single row) and @ columns, our features. For this matrix we construct a variable space with as many dimensions as there are features. Each feature represents one coordinate axis. For each feature, the length has been standardized according to a scaling criterion, normally by scaling to unit variance. It is determinant to scale the features to a common scale, otherwise the features with a greater magnitude will weigh more in determining the principal components. Once plotted all the observations and computed the mean of each variable, that mean will be represented by a point in the center of our plot (the center of gravity). Then, we subtract each observation with the mean, shifting the coordinate system with the center in the origin. The best fitting line resulting is the line that best accounts for the shape of the point swarm. It represents the maximum variance direction in the data. Each observation may be projected onto this line in order to get a coordinate value along the PC-line. This value is known as a score. The next best-fitting line can be similarly chosen from directions perpendicular to the first.
Repeating this process yields an orthogonal basis in which different individual dimensions of the data are uncorrelated. These basis vectors are called principal components.

PCA is mostly used as a tool in exploratory data analysis and for making predictive models. It is often used to visualize genetic distance and relatedness between populations.

SVM (Support Vector Machine)  algorithm

Reference: SVM on wikipedia

Classifying data is a common task in machine learning. Suppose some given data points each belong to one of two classes, and the goal is to decide which class a new data point will be in. In the case of supportvector machines, a data point is viewed as a p-dimensional vector (a list of p numbers), and we want to know whether we can separate such points with a (p − 1)-dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that might classify the data. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes. So, we
choose the hyperplane so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum-margin classifier; or equivalently, the perceptron of optimal stability. The best hyper plane that divides the data is H3.

  • SVMs are helpful in text and hypertext categorization, as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
  • Some methods for shallow semantic parsing are based on support vector machines.
  • Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
  • Classification of satellite data like SAR data using supervised SVM.
  • Hand-written characters can be recognized using SVM.

What are the support vectors in SVM? 

In the diagram, we see that the sketched lines mark the distance from the classifier (the hyper plane) to the closest data points called the support vectors (darkened data points). The distance between the two thin lines is called the margin.

To extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function, max (0, 1 – yi(w∙ xi − b)). This function is zero if x lies on the correct side of the margin. For data on the wrong side of the margin, the function’s value is proportional to the distance from the margin. 

What are the different kernels in SVM?

There are four types of kernels in SVM.
1. LinearKernel
2. Polynomial kernel
3. Radial basis kernel
4. Sigmoid kernel

What are the most known ensemble algorithms? 

Reference: Ensemble Algorithms

The most popular trees are: AdaBoost, Random Forest, and  eXtreme Gradient Boosting (XGBoost).

AdaBoost is best used in a dataset with low noise, when computational complexity or timeliness of results is not a main concern and when there are not enough resources for broader hyperparameter tuning due to lack of time and knowledge of the user.

Random forests should not be used when dealing with time series data or any other data where look-ahead bias should be avoided, and the order and continuity of the samples need to be ensured. This algorithm can handle noise relatively well, but more knowledge from the user is required to adequately tune the algorithm compared to AdaBoost.

The main advantages of XGBoost is its lightning speed compared to other algorithms, such as AdaBoost, and its regularization parameter that successfully reduces variance. But even aside from the regularization parameter, this algorithm leverages a learning rate (shrinkage) and subsamples from the features like random forests, which increases its ability to generalize even further. However, XGBoost is more difficult to understand, visualize and to tune compared to AdaBoost and random forests. There is a multitude of hyperparameters that can be tuned to increase performance.

What is Deep Learning?

Deep Learning is nothing but a paradigm of machine learning which has shown incredible promise in recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of the neurons in the human brain.

What is the difference between machine learning and deep learning?

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning can be categorized in the following four categories.
1. Supervised machine learning,
2. Semi-supervised machine learning,
3. Unsupervised machine learning,
4. Reinforcement learning.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

• The main difference between deep learning and machine learning is due to the way data is
presented in the system. Machine learning algorithms almost always require structured data, while deep learning networks rely on layers of ANN (artificial neural networks).

• Machine learning algorithms are designed to “learn” to act by understanding labeled data and then use it to produce new results with more datasets. However, when the result is incorrect, there is a need to “teach them”. Because machine learning algorithms require bulleted data, they are not suitable for solving complex queries that involve a huge amount of data.

• Deep learning networks do not require human intervention, as multilevel layers in neural
networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes. However, even they can be wrong if the data quality is not good enough.

• Data decides everything. It is the quality of the data that ultimately determines the quality of the result.

• Both of these subsets of AI are somehow connected to data, which makes it possible to represent a certain form of “intelligence.” However, you should be aware that deep learning requires much more data than a traditional machine learning algorithm. The reason for this is that deep learning networks can identify different elements in neural network layers only when more than a million data points interact. Machine learning algorithms, on the other hand, are capable of learning by pre-programmed criteria.

What is the reason for the popularity of Deep Learning in recent times? 

Now although Deep Learning has been around for many years, the major breakthroughs from these techniques came just in recent years. This is because of two main reasons:
• The increase in the amount of data generated through various sources
• The growth in hardware resources required to run these models
GPUs are multiple times faster and they help us build bigger and deeper deep learning models in comparatively less time than we required previously

What is reinforcement learning?

Reinforcement Learning allows to take actions to max cumulative reward. It learns by trial and error through reward/penalty system. Environment rewards agent so by time agent makes better decisions.
Ex: robot=agent, maze=environment. Used for complex tasks (self-driving cars, game AI).

RL is a series of time steps in a Markov Decision Process:

1. Environment: space in which RL operates
2. State: data related to past action RL took
3. Action: action taken
4. Reward: number taken by agent after last action
5. Observation: data related to environment: can be visible or partially shadowed

What are Artificial Neural Networks?

Artificial Neural networks are a specific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks. Neural Networks can adapt to changing the input, so the network generates the best possible result without needing to redesign the output criteria.

Artificial Neural Networks works on the same principle as a biological Neural Network. It consists of inputs which get processed with weighted sums and Bias, with the help of Activation Functions.

How Are Weights Initialized in a Network?

There are two methods here: we can either initialize the weights to zero or assign them randomly.

Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and every layer perform the same operation, giving the same output and making the deep net useless.

Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very close to 0. It gives better accuracy to the model since every neuron performs different computations. This is the most commonly used method.

What Is the Cost Function? 

Also referred to as “loss” or “error,” cost function is a measure to evaluate how good your model’s performance is. It’s used to compute the error of the output layer during backpropagation. We push that error backwards through the neural network and use that during the different training functions.
The most known one is the mean sum of squared errors.

What Are Hyperparameters?

With neural networks, you’re usually working with hyperparameters once the data is formatted correctly.
A hyperparameter is a parameter whose value is set before the learning process begins. It determines how a network is trained and the structure of the network (such as the number of hidden units, the learning rate, epochs, batches, etc.).

What Will Happen If the Learning Rate is Set inaccurately (Too Low or Too High)? 

When your learning rate is too low, training of the model will progress very slowly as we are making minimal updates to the weights. It will take many updates before reaching the minimum point.
If the learning rate is set too high, this causes undesirable divergent behavior to the loss function due to drastic updates in weights. It may fail to converge (model can give a good output) or even diverge (data is too chaotic for the network to train).

Q58: What Is The Difference Between Epoch, Batch, and Iteration in Deep Learning? 

Epoch – Represents one iteration over the entire dataset (everything put into the training model).
Batch – Refers to when we cannot pass the entire dataset into the neural network at once, so we divide the dataset into several batches.
Iteration – if we have 10,000 images as data and a batch size of 200. then an epoch should run 50 iterations (10,000 divided by 50).

Q59:  What Are the Different Layers on CNN?

Reference: Layers of CNN 

The Convolutional neural networks are regularized versions of multilayer perceptron (MLP). They were developed based on the working of the neurons of the animal visual cortex.

The objective of using the CNN:

The idea is that you give the computer this array of numbers and it will output numbers that describe the probability of the image being a certain class (.80 for a cat, .15 for a dog, .05 for a bird, etc.). It works similar to how our brain works. When we look at a picture of a dog, we can classify it as such if the picture has identifiable features such as paws or 4 legs. In a similar way, the computer is able to perform image classification by looking for low-level features such as edges and curves and then building up to more abstract concepts through a series of convolutional layers. The computer uses low-level features obtained at the initial levels to generate high-level features such as paws or eyes to identify the object.

There are four layers in CNN:
1. Convolutional Layer – the layer that performs a convolutional operation, creating several smaller picture windows to go over the data.
2. Activation Layer (ReLU Layer) – it brings non-linearity to the network and converts all the negative pixels to zero. The output is a rectified feature map. It follows each convolutional layer.
3. Pooling Layer – pooling is a down-sampling operation that reduces the dimensionality of the feature map. Stride = how much you slide, and you get the max of the n x n matrix
4. Fully Connected Layer – this layer recognizes and classifies the objects in the image.

What Is Pooling on CNN, and How Does It Work?

Pooling is used to reduce the spatial dimensions of a CNN. It performs down-sampling operations to reduce the dimensionality and creates a pooled feature map by sliding a filter matrix over the input matrix.

What are Recurrent Neural Networks (RNNs)? 

Reference: RNNs

RNNs are a type of artificial neural networks designed to recognize the pattern from the sequence of data such as Time series, stock market and government agencies etc.

Recurrent Neural Networks (RNNs) add an interesting twist to basic neural networks. A vanilla neural network takes in a fixed size vector as input which limits its usage in situations that involve a ‘series’ type input with no predetermined size.

RNNs are designed to take a series of input with no predetermined limit on size. One could ask what’s\ the big deal, I can call a regular NN repeatedly too?

Sure can, but the ‘series’ part of the input means something. A single input item from the series is related to others and likely has an influence on its neighbors. Otherwise it’s just “many” inputs, not a “series” input (duh!).
Recurrent Neural Network remembers the past and its decisions are influenced by what it has learnt from the past. Note: Basic feed forward networks “remember” things too, but they remember things they learnt during training. For example, an image classifier learns what a “1” looks like during training and then uses that knowledge to classify things in production.
While RNNs learn similarly while training, in addition, they remember things learnt from prior input(s) while generating output(s). RNNs can take one or more input vectors and produce one or more output vectors and the output(s) are influenced not just by weights applied on inputs like a regular NN, but also by a “hidden” state vector representing the context based on prior input(s)/output(s). So, the same input could produce a different output depending on previous inputs in the series.

In summary, in a vanilla neural network, a fixed size input vector is transformed into a fixed size output vector. Such a network becomes “recurrent” when you repeatedly apply the transformations to a series of given input and produce a series of output vectors. There is no pre-set limitation to the size of the vector. And, in addition to generating the output which is a function of the input and hidden state, we update the hidden state itself based on the input and use it in processing the next input.

What is the role of the Activation Function?

The Activation function is used to introduce non-linearity into the neural network helping it to learn more complex function. Without which the neural network would be only able to learn linear function which is a linear combination of its input data. An activation function is a function in an artificial neuron that delivers an output based on inputs.

Machine Learning libraries for various purposes

What is an Auto-Encoder?

Reference: Auto-Encoder

Auto-encoders are simple learning networks that aim to transform inputs into outputs with the minimum possible error. This means that we want the output to be as close to input as possible. We add a couple of layers between the input and the output, and the sizes of these layers are smaller than the input layer. The auto-encoder receives unlabeled input which is then encoded to reconstruct the input. 

An autoencoder is a type of artificial neural network used to learn efficient data coding in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name. Several variants exist to the basic model, with the aim of forcing the learned representations of the input to assume useful properties.
Autoencoders are effectively used for solving many applied problems, from face recognition to acquiring the semantic meaning of words.

What is a Boltzmann Machine?

Boltzmann machines have a simple learning algorithm that allows them to discover interesting features that represent complex regularities in the training data. The Boltzmann machine is basically used to optimize the weights and the quantity for the given problem. The learning algorithm is very slow in networks with many layers of feature detectors. “Restricted Boltzmann Machines” algorithm has a single layer of feature detectors which makes it faster than the rest.

What Is Dropout and Batch Normalization?

Dropout is a technique of dropping out hidden and visible nodes of a network randomly to prevent overfitting of data (typically dropping 20 per cent of the nodes). It doubles the number of iterations needed to converge the network. It used to avoid overfitting, as it increases the capacity of generalization.

Batch normalization is the technique to improve the performance and stability of neural networks by normalizing the inputs in every layer so that they have mean output activation of zero and standard deviation of one

Why Is TensorFlow the Most Preferred Library in Deep Learning?

TensorFlow provides both C++ and Python APIs, making it easier to work on and has a faster compilation time compared to other Deep Learning libraries like Keras and PyTorch. TensorFlow supports both CPU and GPU computing devices.

What is Tensor in TensorFlow?

A tensor is a mathematical object represented as arrays of higher dimensions. Think of a n-D matrix. These arrays of data with different dimensions and ranks fed as input to the neural network are called “Tensors.”

What is the Computational Graph?

Everything in a TensorFlow is based on creating a computational graph. It has a network of nodes where each node operates. Nodes represent mathematical operations, and edges represent tensors. Since data flows in the form of a graph, it is also called a “DataFlow Graph.”

How is logistic regression done? 

Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid).

Explain the steps in making a decision tree. 

1. Take the entire data set as input
2. Calculate entropy of the target variable, as well as the predictor attributes
3. Calculate your information gain of all attributes (we gain information on sorting different objects from each other)
4. Choose the attribute with the highest information gain as the root node
5. Repeat the same procedure on every branch until the decision node of each branch is finalized
For example, let’s say you want to build a decision tree to decide whether you should accept or decline a job offer. The decision tree for this case is as shown:

It is clear from the decision tree that an offer is accepted if:
• Salary is greater than $50,000
• The commute is less than an hour
• Coffee is offered

How do you build a random forest model?

A random forest is built up of a number of decision trees. If you split the data into different packages and make a decision tree in each of the different groups of data, the random forest brings all those trees together.

Steps to build a random forest model:

1. Randomly select ; features from a total of = features where  k<< m
2. Among the ; features, calculate the node D using the best split point
3. Split the node into daughter nodes using the best split
4. Repeat steps two and three until leaf nodes are finalized
5. Build forest by repeating steps one to four for > times to create > number of trees

Differentiate between univariate, bivariate, and multivariate analysis. 

Univariate data contains only one variable. The purpose of the univariate analysis is to describe the data and find patterns that exist within it.

The patterns can be studied by drawing conclusions using mean, median, mode, dispersion or range, minimum, maximum, etc.

Bivariate data involves two different variables. The analysis of this type of data deals with causes and relationships and the analysis is done to determine the relationship between the two variables.

Here, the relationship is visible from the table that temperature and sales are directly proportional to each other. The hotter the temperature, the better the sales.

Multivariate data involves three or more variables, it is categorized under multivariate. It is similar to a bivariate but contains more than one dependent variable.

Example: data for house price prediction
The patterns can be studied by drawing conclusions using mean, median, and mode, dispersion or range, minimum, maximum, etc. You can start describing the data and using it to guess what the price of the house will be.

What are the feature selection methods used to select the right variables?

There are two main methods for feature selection.
Filter Methods
This involves:
• Linear discrimination analysis
• ANOVA
• Chi-Square
The best analogy for selecting features is “bad data in, bad answer out.” When we’re limiting or selecting the features, it’s all about cleaning up the data coming in.

Wrapper Methods
This involves:
• Forward Selection: We test one feature at a time and keep adding them until we get a good fit
• Backward Selection: We test all the features and start removing them to see what works
better
• Recursive Feature Elimination: Recursively looks through all the different features and how they pair together

Wrapper methods are very labor-intensive, and high-end computers are needed if a lot of data analysis is performed with the wrapper method.

You are given a data set consisting of variables with more than 30 percent missing values. How will you deal with them? 

If the data set is large, we can just simply remove the rows with missing data values. It is the quickest way; we use the rest of the data to predict the values.

For smaller data sets, we can impute missing values with the mean, median, or average of the rest of the data using pandas data frame in python. There are different ways to do so, such as: df.mean(), df.fillna(mean)

Other option of imputation is using KNN for numeric or classification values (as KNN just uses k closest values to impute the missing value).

Q76: How will you calculate the Euclidean distance in Python?

plot1 = [1,3]

plot2 = [2,5]

The Euclidean distance can be calculated as follows:

euclidean_distance = sqrt((plot1[0]-plot2[0])**2 + (plot1[1]- plot2[1])**2)

What are dimensionality reduction and its benefits? 

Dimensionality reduction refers to the process of converting a data set with vast dimensions into data with fewer dimensions (fields) to convey similar information concisely.

This reduction helps in compressing data and reducing storage space. It also reduces computation time as fewer dimensions lead to less computing. It removes redundant features; for example, there’s no point in storing a value in two different units (meters and inches).

How should you maintain a deployed model?

The steps to maintain a deployed model are (CREM):

1. Monitor: constant monitoring of all models is needed to determine their performance accuracy.
When you change something, you want to figure out how your changes are going to affect things.
This needs to be monitored to ensure it’s doing what it’s supposed to do.
2. Evaluate: evaluation metrics of the current model are calculated to determine if a new algorithm is needed.
3. Compare: the new models are compared to each other to determine which model performs the best.
4. Rebuild: the best performing model is re-built on the current state of data.

How can a time-series data be declared as stationery?

  1. The mean of the series should not be a function of time.
  1. The variance of the series should not be a function of time. This property is known as homoscedasticity.
  1. The covariance of the i th term and the (i+m) th term should not be a function of time.

‘People who bought this also bought…’ recommendations seen on Amazon are a result of which algorithm?

The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains the behavior of other users and their purchase history in terms of ratings, selection, etc.
The engine makes predictions on what might interest a person based on the preferences of other users. In this algorithm, item features are unknown.
For example, a sales page shows that a certain number of people buy a new phone and also buy tempered glass at the same time. Next time, when a person buys a phone, he or she may see a recommendation to buy tempered glass as well.

What is a Generative Adversarial Network?

Suppose there is a wine shop purchasing wine from dealers, which they resell later. But some dealers sell fake wine. In this case, the shop owner should be able to distinguish between fake and authentic wine. The forger will try different techniques to sell fake wine and make sure specific techniques go past the shop owner’s check. The shop owner would probably get some feedback from wine experts that some of the wine is not original. The owner would have to improve how he determines whether a wine is fake or authentic.
The forger’s goal is to create wines that are indistinguishable from the authentic ones while the shop owner intends to tell if the wine is real or not accurately.

• There is a noise vector coming into the forger who is generating fake wine.
• Here the forger acts as a Generator.
• The shop owner acts as a Discriminator.
• The Discriminator gets two inputs; one is the fake wine, while the other is the real authentic wine.
The shop owner has to figure out whether it is real or fake.

So, there are two primary components of Generative Adversarial Network (GAN) named:
1. Generator
2. Discriminator

The generator is a CNN that keeps keys producing images and is closer in appearance to the real images while the discriminator tries to determine the difference between real and fake images. The ultimate aim is to make the discriminator learn to identify real and fake images.

You are given a dataset on cancer detection. You have built a classification model and achieved an accuracy of 96 percent. Why shouldn’t you be happy with your model performance? What can you do about it?

Cancer detection results in imbalanced data. In an imbalanced dataset, accuracy should not be based as a measure of performance. It is important to focus on the remaining four percent, which represents the patients who were wrongly diagnosed. Early diagnosis is crucial when it comes to cancer detection and can greatly improve a patient’s prognosis.

Hence, to evaluate model performance, we should use Sensitivity (True Positive Rate), Specificity (True Negative Rate), F measure to determine the class wise performance of the classifier.

We want to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate algorithm for this case?

The most appropriate algorithm for this case is logistic regression.

After studying the behavior of a population, you have identified four specific individual types that are valuable to your study. You would like to find all users who are most similar to each individual type. Which algorithm is most appropriate for this study? 

As we are looking for grouping people together specifically by four different similarities, it indicates the value of k. Therefore, K-means clustering is the most appropriate algorithm for this study.

You have run the association rules algorithm on your dataset, and the two rules {banana, apple} => {grape} and {apple, orange} => {grape} have been found to be relevant. What else must be true? 

{grape, apple} must be a frequent itemset.

Your organization has a website where visitors randomly receive one of two coupons. It is also possible that visitors to the website will not receive a coupon. You have been asked to determine if offering a coupon to website visitors has any impact on their purchase decisions. Which analysis method should you use?

One-way ANOVA: in statistics, one-way analysis of variance is a technique that can be used to compare means of two or more samples. This technique can be used only for numerical response data, the “Y”, usually one variable, and numerical or categorical input data, the “X”, always one variable, hence “oneway”.
The ANOVA tests the null hypothesis, which states that samples in all groups are drawn from populations with the same mean values. To do this, two estimates are made of the population variance. The ANOVA produces an F-statistic, the ratio of the variance calculated among the means to the variance within the samples. If the group means are drawn from populations with the same mean values, the variance between the group means should be lower than the variance of the samples, following the central limit
theorem. A higher ratio therefore implies that the samples were drawn from populations with different mean values.

What are the feature vectors?

A feature vector is an n-dimensional vector of numerical features that represent an object. In machine learning, feature vectors are used to represent numeric or symbolic characteristics (called features) of an object in a mathematical way that’s easy to analyze.

What is root cause analysis?

Root cause analysis was initially developed to analyze industrial accidents but is now widely used in other areas. It is a problem-solving technique used for isolating the root causes of faults or problems. A factor is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event from recurring.

Do gradient descent methods always converge to similar points?

They do not, because in some cases, they reach a local minimum or a local optimum point. You would not reach the global optimum point. This is governed by the data and the starting conditions.

What are the different Deep Learning Frameworks?

PyTorch: PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. It is free and open-source software released under the Modified BSD license.
TensorFlow: TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. Licensed by Apache License 2.0. Developed by Google Brain Team.
Microsoft Cognitive Toolkit: Microsoft Cognitive Toolkit describes neural networks as a series of computational steps via a directed graph.
Keras: Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. Licensed by MIT.

What are the different Deep Learning Frameworks?

PyTorch: PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. It is free and open-source software released under the Modified BSD license.
TensorFlow: TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. Licensed by Apache License 2.0. Developed by Google Brain Team.
Microsoft Cognitive Toolkit: Microsoft Cognitive Toolkit describes neural networks as a series of computational steps via a directed graph.
Keras: Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. Licensed by MIT.

How Does an LSTM Network Work?

Reference: LTSM

Long-Short-Term Memory (LSTM) is a special kind of recurrent neural network capable of learning long-term dependencies, remembering information for long periods as its default behavior. There are three steps in an LSTM network:
• Step 1: The network decides what to forget and what to remember.
• Step 2: It selectively updates cell state values.
• Step 3: The network decides what part of the current state makes it to the output.

What Is a Multi-layer Perceptron (MLP)?

Reference: MLP

As in Neural Networks, MLPs have an input layer, a hidden layer, and an output layer. It has the same structure as a single layer perceptron with one or more hidden layers.

Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks.
A (single layer) perceptron is a single layer neural network that works as a linear binary classifier. Being a single layer neural network, it can be trained without the use of more advanced algorithms like back propagation and instead can be trained by “stepping towards” your error in steps specified by a learning rate. When someone says perceptron, I usually think of the single layer version.

Machine Learning Multi-Layer Perceptron

What is exploding gradients? 

While training an RNN, if you see exponentially growing (very large) error gradients which accumulate and result in very large updates to neural network model weights during training, they’re known as exploding gradients. At an extreme, the values of weights can become so large as to overflow and result in NaN values. The explosion occurs through exponential growth by repeatedly multiplying gradients through the network layers that have values larger than 1.0.
This has the effect of your model is unstable and unable to learn from your training data.
There are some subtle signs that you may be suffering from exploding gradients during the training of your network, such as:
• The model is unable to get traction on your training data (e.g. poor loss).
• The model is unstable, resulting in large changes in loss from update to update.
• The model loss goes to NaN during training.
• The model weights quickly become very large during training.
• The error gradient values are consistently above 1.0 for each node and layer during training.

Solutions
1. Re-Design the Network Model:
a. In deep neural networks, exploding gradients may be addressed by redesigning the
network to have fewer layers. There may also be some benefit in using a smaller batch
size while training the network.
b. In RNNs, updating across fewer prior time steps during training, called truncated
Backpropagation through time, may reduce the exploding gradient problem.

2. Use Long Short-Term Memory Networks: In RNNs, exploding gradients can be reduced by using the Long Short-Term Memory (LSTM) memory units and perhaps related gated-type neuron structures. Adopting LSTM memory units is a new best practice for recurrent neural networks for sequence prediction.

3. Use Gradient Clipping: Exploding gradients can still occur in very deep Multilayer Perceptron networks with a large batch size and LSTMs with very long input sequence lengths. If exploding gradients are still occurring, you can check for and limit the size of gradients during the training of your network. This is called gradient clipping. Specifically, the values of the error gradient are checked against a threshold value and clipped or set to that threshold value if the error gradient exceeds the threshold.

4. Use Weight Regularization: another approach, if exploding gradients are still occurring, is to check the size of network weights and apply a penalty to the networks loss function for large weight values. This is called weight regularization and often an L1 (absolute weights) or an L2 (squared weights) penalty can be used.

What is vanishing gradients? 

While training an RNN, your slope can become either too small; this makes the training difficult. When the slope is too small, the problem is known as a Vanishing Gradient. It leads to long training times, poor performance, and low accuracy.
• Hyperbolic tangent and Sigmoid/Soft-max suffer vanishing gradient.
• RNNs suffer vanishing gradient, LSTM no (so it is perfect to predict stock prices). In fact, the propagation of error through previous layers makes the gradient get smaller so the weights are not updated.

Solutions
1. Choose RELU
2. Use LSTM (for RNNs)
3. Use ResNet (Residual Network) → after some layers, add x again: F(x) → ⋯ → F(x) + x
4. Multi-level hierarchy: pre-train one layer at the time through unsupervised learning, then fine-tune via backpropagation
5. Gradient checking: debugging strategy used to numerically track and assess gradients during training.

What is Gradient Descent?

Let’s first explain what a gradient is. A gradient is a mathematical function. When calculated on a point of a function, it gives the hyperplane (or slope) of the directions in which the function increases more. The gradient vector can be interpreted as the “direction and rate of fastest increase”. If the gradient of a function is non-zero at a point p, the direction of the gradient is the direction in which the function increases most quickly from p, and the magnitude of the gradient is the rate of increase in that direction.
Further, the gradient is the zero vector at a point if and only if it is a stationary point (where the derivative vanishes).
In Data Science, it simply measures the change in all weights with regard to the change in error, as we are partially derivating by w the loss function.

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.

Machine Learning Gradient Descent

The goal of the gradient descent is to minimize a given function which, in our case, is the loss function of the neural network. To achieve this goal, it performs two steps iteratively.
1. Compute the slope (gradient) that is the first-order derivative of the function at the current point
2. Move-in the opposite direction of the slope increase from the current point by the computed amount
So, the idea is to pass the training set through the hidden layers of the neural network and then update the parameters of the layers by computing the gradients using the training samples from the training dataset.
Think of it like this. Suppose a man is at top of the valley and he wants to get to the bottom of the valley.
So, he goes down the slope. He decides his next position based on his current position and stops when he gets to the bottom of the valley which was his goal.

What is vanishing gradients? 

While training an RNN, your slope can become either too small; this makes the training difficult. When the slope is too small, the problem is known as a Vanishing Gradient. It leads to long training times, poor performance, and low accuracy.
• Hyperbolic tangent and Sigmoid/Soft-max suffer vanishing gradient.
• RNNs suffer vanishing gradient, LSTM no (so it is perfect to predict stock prices). In fact, the propagation of error through previous layers makes the gradient get smaller so the weights are not updated.

Solutions
1. Choose RELU
2. Use LSTM (for RNNs)
3. Use ResNet (Residual Network) → after some layers, add x again: F(x) → ⋯ → F(x) + x
4. Multi-level hierarchy: pre-train one layer at the time through unsupervised learning, then fine-tune via backpropagation
5. Gradient checking: debugging strategy used to numerically track and assess gradients during training.

What is Back Propagation and Explain it Works. 

Back propagation is a training algorithm used for neural network. In this method, we update the weights of each layer from the last layer recursively, with the formula:

Machine Learning Back Propagation Formula

It has the following steps:
• Forward Propagation of Training Data (initializing weights with random or pre-assigned values)
• Gradients are computed using output weights and target
• Back Propagate for computing gradients of error from output activation
• Update the Weights

What are the variants of Back Propagation? 

Reference: Variants of back propagation

  • Stochastic Gradient Descent: In Batch Gradient Descent we were considering all the examples for every step of Gradient Descent. But what if our dataset is very huge. Deep learning models crave for data. The more the data the more chances of a model to be good. Suppose our dataset has 5 million examples, then just to take one step the model will have to calculate the gradients of all the 5 million examples. This does not seem an efficient way. To tackle this problem, we have Stochastic Gradient Descent. In Stochastic Gradient Descent (SGD), we consider just one example at a time to take a single step. We do the following steps in one epoch for SGD:
    1. Take an example
    2. Feed it to Neural Network
    3. Calculate its gradient
    4. Use the gradient we calculated in step 3 to update the weights
    5. Repeat steps 1–4 for all the examples in training dataset
    Since we are considering just one example at a time the cost will fluctuate over the training examples and it will not necessarily decrease. But in the long run, you will see the cost decreasing with fluctuations. Also, because the cost is so fluctuating, it will never reach the minimum, but it will keep dancing around it. SGD can be used for larger datasets. It converges faster when the dataset is large as it causes updates to the parameters more frequently.

 Stochastic Gradient Descent (SGD)

Stochastic Gradient Descent (SGD)
  • Batch Gradient Descent: all the training data is taken into consideration to take a single step. We take the average of the gradients of all the training examples and then use that mean gradient to update our parameters. So that’s just one step of gradient descent in one epoch. Batch Gradient Descent is great for convex or relatively smooth error manifolds. In this case, we move somewhat directly towards an optimum solution. The graph of cost vs epochs is also quite smooth because we are averaging over all the gradients of training data for a single step. The cost keeps on decreasing over the epochs.
Batch Gradient Descent
  • Mini-batch Gradient Descent: It’s one of the most popular optimization algorithms. It’s a variant of Stochastic Gradient Descent and here instead of single training example, mini batch of samples is used. Batch Gradient Descent can be used for smoother curves. SGD can be used when the dataset is large. Batch Gradient Descent converges directly to minima. SGD converges faster for larger datasets.
    But, since in SGD we use only one example at a time, we cannot implement the vectorized implementation on it. This can slow down the computations. To tackle this problem, a mixture of Batch Gradient Descent and SGD is used. Neither we use all the dataset all at once nor we use the single example at a time. We use a batch of a fixed number of training examples which is less than the actual dataset and call it a mini-batch. Doing this helps us achieve the advantages of both the former variants we saw. So, after creating the mini-batches of fixed size, we do the following steps in one epoch:
    1. Pick a mini-batch
    2. Feed it to Neural Network
    3. Calculate the mean gradient of the mini-batch
    4. Use the mean gradient we calculated in step 3 to update the weights
    5. Repeat steps 1–4 for the mini-batches we created
    Just like SGD, the average cost over the epochs in mini-batch gradient descent fluctuates because we are averaging a small number of examples at a time. So, when we are using the mini-batch gradient descent we are updating our parameters frequently as well as we can use vectorized implementation for faster computations.

References:

800 Data Science Questions & Answers doc by Steve Nouri

While we continue to integrate ML systems in high-stakes environments such as medical settings, roads, command control centers, we need to ensure they do not cause the loss of life. How can you handle this?

By focusing on the following, which includes everything outside of just developing SOTA models, as well inclusion of key stakeholders.

🔹Robustness: Create models that are resilient to adversaries, unusual situations, and Black Swan events

🔹Monitoring: Detect malicious use, monitor predictions, and discover unexpected model functionality

🔹Alignment: Build models that represent and safely optimize hard-to-specify human values

🔹External Safety: Use ML to address risks to how ML systems are handled, such as cyber attacks

Machine Learning Unsolved Problems_ n_Safety

Download Unsolved Problems in ML Safety Here

You are given a data set. The data set has missing values that spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?

Since the data is spread across the median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.

What are PCA, KPCA, and ICA used for?

PCA (Principal Components Analysis), KPCA ( Kernel-based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction.

What are support vector machines?

Support vector machines are supervised learning algorithms used for classification and regression analysis.

What is batch statistical learning?

Statistical learning techniques allow learning a function or predictor from a set of observed data that can make predictions about unseen or future data. These techniques provide guarantees on the performance of the learned predictor on the future unseen data based on a statistical assumption on the data generating process.

What is the bias-variance decomposition of classification error in the ensemble method?

The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets.

When is Ridge regression favorable over Lasso regression?

You can quote ISLR’s authors Hastie, Tibshirani who asserted that, in the presence of few variables with medium / large sized effect, use lasso regression. In presence of many variables with small/medium-sized effects, use ridge regression.
Conceptually, we can say, lasso regression (L1) does both variable selection and parameter shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all the coefficients in the model. In the presence of correlated variables, ridge regression might be the preferred choice. Also, ridge regression works best in situations where the least square estimates have higher variance. Therefore, it depends on our model objective.

You’ve built a random forest model with 10000 trees. You got delighted after getting training error as 0.00. But, the validation error is 34.23. What is going on? Haven’t you trained your model perfectly?

The model has overfitted. Training error 0.00 means the classifier has mimicked the training data patterns to an extent, that they are not available in the unseen data. Hence, when this classifier was run on an unseen sample, it couldn’t find those patterns and returned predictions with higher error. In a random forest, it happens when we use a larger number of trees than necessary. Hence, to avoid this situation, we should tune the number of trees using cross-validation.

What is a convex hull?

In the case of linearly separable data, the convex hull represents the outer boundaries of the two groups of data points. Once the convex hull is created, we get maximum margin hyperplane (MMH) as a perpendicular bisector between two convex hulls. MMH is the line which attempts to create the greatest separation between two groups.

What do you understand by Type I vs Type II error?

Type I error is committed when the null hypothesis is true and we reject it, also known as a ‘False Positive’. Type II error is committed when the null hypothesis is false and we accept it, also known as ‘False Negative’.
In the context of the confusion matrix, we can say Type I error occurs when we classify a value as positive (1) when it is actually negative (0). Type II error occurs when we classify a value as negative (0) when it is actually positive(1).

In k-means or kNN, we use euclidean distance to calculate the distance between nearest neighbors. Why not manhattan distance?

We don’t use manhattan distance because it calculates distance horizontally or vertically only. It has dimension restrictions. On the other hand, the euclidean metric can be used in any space to calculate distance. Since the data points can be present in any dimension, euclidean distance is a more viable option.

Example: Think of a chessboard, the movement made by a bishop or a rook is calculated by manhattan distance because of their respective vertical & horizontal movements.

Do you suggest that treating a categorical variable as a continuous variable would result in a better predictive model?

For better predictions, the categorical variable can be considered as a continuous variable only when the variable is ordinal in nature.

OLS is to linear regression wha the maximum likelihood is logistic regression. Explain the statement.

OLS and Maximum likelihood are the methods used by the respective regression methods to approximate the unknown parameter (coefficient) value. In simple words, Ordinary least square(OLS) is a method used in linear regression which approximates the parameters resulting in minimum distance between actual and predicted values. Maximum
Likelihood helps in choosing the values of parameters which maximizes the likelihood that the parameters are most likely to produce observed data.

When does regularization becomes necessary in Machine Learning?

Regularization becomes necessary when the model begins to overfit/underfit. This technique introduces a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and hence reduce the cost term. This helps to reduce model complexity so that the model can become better at predicting (generalizing).

What is Linear Regression?

Linear Regression is a supervised Machine Learning algorithm. It is used to find the linear relationship between the dependent and the independent variables for predictive analysis.

What is the Variance Inflation Factor?

Variance Inflation Factor (VIF) is the estimate of the volume of multicollinearity in a collection of many regression variables.
VIF = Variance of the model / Variance of the model with a single independent variable
We have to calculate this ratio for every independent variable. If VIF is high, then it shows the high collinearity of the independent variables.

We know that one hot encoding increases the dimensionality of a dataset, but label encoding doesn’t. How?

When we use one-hot encoding, there is an increase in the dimensionality of a dataset. The reason for the increase in dimensionality is that, for every class in the categorical variables, it forms a different variable.

What is a Decision Tree?

A decision tree is used to explain the sequence of actions that must be performed to get the desired output. It is a hierarchical diagram that shows the actions.

What is the Binarizing of data? How to Binarize?

In most of the Machine Learning Interviews, apart from theoretical questions, interviewers focus on the implementation part. So, this ML Interview Questions focused on the implementation of the theoretical concepts.
Converting data into binary values on the basis of threshold values is known as the binarizing of data. The values that are less than the threshold are set to 0 and the values that are greater than the threshold are set to 1.
This process is useful when we have to perform feature engineering, and we can also use it for adding unique features.

What is cross-validation?

Cross-validation is essentially a technique used to assess how well a model performs on a new independent dataset. The simplest example of cross-validation is when you split your data into two groups: training data and testing data, where you use the training data to build the model and the testing data to test the model.

When would you use random forests Vs SVM and why?

There are a couple of reasons why a random forest is a better choice of the model than a support vector machine:
● Random forests allow you to determine the feature importance. SVM’s can’t do this.
● Random forests are much quicker and simpler to build than an SVM.
● For multi-class classification problems, SVMs require a one-vs-rest method, which is less scalable and more memory intensive.

What are the drawbacks of a linear model?

There are a couple of drawbacks of a linear model:
● A linear model holds some strong assumptions that may not be true in the application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
● A linear model can’t be used for discrete or binary outcomes.
● You can’t vary the model flexibility of a linear model.

What is the programming model and best language for Hadoop and Spark? Python or Java?

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Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. Apache Hadoop is used mainly for Data Analysis

Apache Spark is an open-source distributed general-purpose cluster-computing framework. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance

The question is Which programming language is good to drive Hadoop and Spark?

The programming model for developing hadoop based applications is the map reduce. In other words, MapReduce is the processing layer of Hadoop.
MapReduce programming model is designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks. Hadoop MapReduce is a software framework for easily writing an application that processes the vast amount of structured and unstructured data stored in the Hadoop Distributed FileSystem (HDFS). The biggest advantage of map reduce is to make data processing on multiple computing nodes easy. Under the Map reduce model, data processing primitives are called Mapper and Reducers.

Spark is written in Scala and Hadoop is written in Java.

The key difference between Hadoop MapReduce and Spark lies in the approach to processing: Spark can do it in-memory, while Hadoop MapReduce has to read from and write to a disk. As a result, the speed of processing differs significantly – Spark may be up to 100 times faster.

In-memory processing is faster when compared to Hadoop, as there is no time spent in moving data/processes in and out of the disk. Spark is 100 times faster than MapReduce as everything is done here in memory.

Spark’s hardware is more expensive than Hadoop MapReduce because it’s hardware needs a lot of RAM.

Hadoop runs on Linux, it means that you must have knowldge of linux.

Java is important for hadoop because:

  • There are some advanced features that are only available via the Java API.
  • The ability to go deep into the Hadoop coding and figure out what’s going wrong.

In both these situations, Java becomes very important.
As a developer, you can enjoy many advanced features of Spark and Hadoop if you start with their native languages (Java and Scala).

What Python Offers for Hadoop and Spark?

  • Simple syntax– Python offers simple syntax which shows it is more user friendly than other two languages.
  • Easy to learn – Python syntax are like English languages. So, it much more easier to learn it and master it.
  • Large community support – Unlike Scala, Python has huge community (active), which we will help you to solve your queries.
  • Offers Libraries, frameworks and packages – Python has huge number of Scientific packages, libraries and framework, which are helping you to work in any environment of Hadoop and Spark.
  • Python Compatibility with Hadoop – A package called PyDoop offers access to the HDFS API for Hadoop and hence it allows to write Hadoop MapReduce program and application.
  • Hadoop is based off of Java (then so e.g. non-Hadoop yet still a Big-Data technology like the ElasticSearch engine, too – even though it processes JSON REST requests)
  • Spark is created off of Scala although pySpark (the lovechild of Python and Spark technologies of course) has gained a lot of momentum as of late.

If you are planning for Hadoop Data Analyst, Python is preferable given that it has many libraries to perform advanced analytics and also you can use Spark to perform advanced analytics and implement machine learning techniques using pyspark API.

The key Value pair is the record entity that MapReduce job receives for execution. In MapReduce process, before passing the data to the mapper, data should be first converted into key-value pairs as mapper only understands key-value pairs of data.
key-value pairs in Hadoop MapReduce is generated as follows:

Resources:

1- Quora

2- Wikipedia

3- Data Flair

AWS Developer and Deployment Theory: Facts and Summaries and Questions/Answers

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AWS Developer – Deployment Theory Facts and summaries, Top 80 AWS Developer Theory Questions and Answers Dump

Definition 1: The AWS Developer is responsible for designing, deploying, and developing cloud applications on AWS platform

Definition 2: The AWS Developer Tools is a set of services designed to enable developers and IT operations professionals practicing DevOps to rapidly and safely deliver software.




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AWS Developer and Deployment Theory Facts and summaries

    1. Continuous Integration is about integrating or merging the code changes frequently, at least once per day. It enables multiple devs to work on the same application.
    2. Continuous delivery is all about automating the build, test, and deployment functions.
    3. Continuous Deployment fully automates the entire release process, code is deployed into Production as soon as it has successfully passed through the release pipeline.
    4. AWS CodePipeline is a continuous integration/Continuous delivery service:
      • It automates your end-to-end software release process based on user defines workflow
      • It can be configured to automatically trigger your pipeline as soon as a change is detected in your source code repository
      • It integrates with other services from AWS like CodeBuild and CodeDeploy, as well as third party custom plug-ins.
    5. AWS CodeBuild is a fully managed build service. It can build source code, run tests and produce software packages based on commands that you define yourself.
    6. Dy default the buildspec.yml defines the build commands and settings used by CodeBuild to run your build.
    7. AWS CodeDeploy is a fully managed automated deployment service and can be used as part of a Continuous Delivery or Continuous Deployment process.
    8. There are 2 types of deployment approach:
      • In-place or Rolling update- you stop the application on each host and deploy the latest code. EC2 and on premise systems only. To roll back, you must re-deploy the previous version of the application.
      • Blue/Green : New instances are provisioned and the new application is deployed to these new instances. Traffic is routed to the new instances according to your own schedule. Supported for EC2, on-premise systems and Lambda functions. Rollback is easy, just route the traffic back to the original instances. Blue is active deployment, green is new release.
    9. Docker allows you to package your software into Containers which you can run in Elastic Container Service (ECS)
    10.  A docker Container includes everything the software needs to run including code, libraries, runtime and environment variables etc..
    11.  A special file called Dockerfile is used to specify the instructions needed to assemble your Docker image.
    12.  Once built, Docker images can be stored in Elastic Container Registry (ECR) and ECS can then use the image to launch Docker Containers.
    13. AWS CodeCommit is based on Git. It provides centralized repositories for all your code, binaries, images, and libraries.
    14. CodeCommit tracks and manages code changes. It maintains version history.
    15. CodeCommit manages updates from multiple sources and enables collaboration.
    16. To support CORS, API resource needs to implement an OPTIONS method that can respond to the OPTIONS preflight request with following headers:

        

      • Access-Control-Allow-Headers
      • Access-Control-Allow-Origin
      • Access-Control-Allow-Methods
    17. You have a legacy application that works via XML messages. You need to place the application behind the API gateway in order for customers to make API calls. Which of the following would you need to configure?
      You will need to work with the Request and Response Data mapping.
    18. Your application currently points to several Lambda functions in AWS. A change is being made to one of the Lambda functions. You need to ensure that application traffic is shifted slowly from one Lambda function to the other. Which of the following steps would you carry out?
      • Create an ALIAS with the –routing-config parameter
      • Update the ALIAS with the –routing-config parameter

      By default, an alias points to a single Lambda function version. When the alias is updated to point to a different function version, incoming request traffic in turn instantly points to the updated version. This exposes that alias to any potential instabilities introduced by the new version. To minimize this impact, you can implement the routing-config parameter of the Lambda alias that allows you to point to two different versions of the Lambda function and dictate what percentage of incoming traffic is sent to each version.

    19. AWS CodeDeploy: The AppSpec file defines all the parameters needed for the deployment e.g. location of application files and pre/post deployment validation tests to run.
    20. For Ec2 / On Premise systems, the appspec.yml file must be placed in the root directory of your revision (the same folder that contains your application code). Written in YAML.
    21. For Lambda and ECS deployment, the AppSpec file can be YAML or JSON
    22. Visual workflows are automatically created when working with which Step Functions
    23. API Gateway stages store configuration for deployment. An API Gateway Stage refers to A snapshot of your API
    24. AWS SWF Services SWF guarantees delivery order of messages/tasks
    25. Blue/Green Deployments with CodeDeploy on AWS Lambda can happen in multiple ways. Which of these is a potential option? Linear, All at once, Canary
    26. X-Ray Filter Expressions allow you to search through request information using characteristics like URL Paths, Trace ID, Annotations
    27. S3 has eventual consistency for overwrite PUTS and DELETES.
    28. What can you do to ensure the most recent version of your Lambda functions is in CodeDeploy?
      Specify the version to be deployed in AppSpec file.

      https://docs.aws.amazon.com/codedeploy/latest/userguide/application-specification-files.htmlAppSpec Files on an Amazon ECS Compute Platform  

      If your application uses the Amazon ECS compute platform, the AppSpec file can be formatted with either YAML or JSON. It can also be typed directly into an editor in the console. The AppSpec file is used to specify:

      The name of the Amazon ECS service and the container name and port used to direct traffic to the new task set. The functions to be used as validation tests. You can run validation Lambda functions after deployment lifecycle events. For more information, see AppSpec 'hooks' Section for an Amazon ECS Deployment, AppSpec File Structure for Amazon ECS Deployments , and AppSpec File Example for an Amazon ECS Deployment .

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    Reference: AWS Developer Tools




    AWS Developer and Deployment Theory: Top 80 Questions and Answers Dump

    Q0: Which AWS service can be used to compile source code, run tests and package code?

    • A. CodePipeline
    • B. CodeCommit
    • C. CodeBuild
    • D. CodeDeploy

    Answer: C.

    Reference: AWS CodeBuild

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    Q1: How can your prevent CloudFormation from deleting your entire stack on failure? (Choose 2)

    • A. Set the Rollback on failure radio button to No in the CloudFormation console
    • B. Set Termination Protection to Enabled in the CloudFormation console
    • C. Use the –disable-rollback flag with the AWS CLI
    • D. Use the –enable-termination-protection protection flag with the AWS CLI

    Answer: A. and C.

    Reference: Protecting a Stack From Being Deleted

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    Q2: Which of the following practices allows multiple developers working on the same application to merge code changes frequently, without impacting each other and enables the identification of bugs early on in the release process?

    • A. Continuous Integration
    • B. Continuous Deployment
    • C. Continuous Delivery
    • D. Continuous Development

    Answer: A

    Reference: What is Continuous Integration?

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    Q3: When deploying application code to EC2, the AppSpec file can be written in which language?

    • A. JSON
    • B. JSON or YAML
    • C. XML
    • D. YAML

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    Q4: Part of your CloudFormation deployment fails due to a mis-configuration, by defaukt what will happen?

    • A. CloudFormation will rollback only the failed components
    • B. CloudFormation will rollback the entire stack
    • C. Failed component will remain available for debugging purposes
    • D. CloudFormation will ask you if you want to continue with the deployment

    Answer: B

    Reference: Troubleshooting AWS CloudFormation

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    Q5: You want to receive an email whenever a user pushes code to CodeCommit repository, how can you configure this?

    • A. Create a new SNS topic and configure it to poll for CodeCommit eveents. Ask all users to subscribe to the topic to receive notifications
    • B. Configure a CloudWatch Events rule to send a message to SES which will trigger an email to be sent whenever a user pushes code to the repository.
    • C. Configure Notifications in the console, this will create a CloudWatch events rule to send a notification to a SNS topic which will trigger an email to be sent to the user.
    • D. Configure a CloudWatch Events rule to send a message to SQS which will trigger an email to be sent whenever a user pushes code to the repository.

    Answer: C

    Reference: Getting Started with Amazon SNS

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    Q6: Which AWS service can be used to centrally store and version control your application source code, binaries and libraries

    • A. CodeCommit
    • B. CodeBuild
    • C. CodePipeline
    • D. ElasticFileSystem

    Answer: A

    Reference: AWS CodeCommit

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    Q7: You are using CloudFormation to create a new S3 bucket,
    which of the following sections would you use to define the properties of your bucket?

    • A. Conditions
    • B. Parameters
    • C. Outputs
    • D. Resources

    Answer: D

    Reference: Resources

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    Q8: You are deploying a number of EC2 and RDS instances using CloudFormation. Which section of the CloudFormation template
    would you use to define these?

    • A. Transforms
    • B. Outputs
    • C. Resources
    • D. Instances

    Answer: C.
    The Resources section defines your resources you are provisioning. Outputs is used to output user defines data relating to the resources you have built and can also used as input to another CloudFormation stack. Transforms is used to reference code located in S3.

    Reference: Resources

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    Q9: Which AWS service can be used to fully automate your entire release process?

    • A. CodeDeploy
    • B. CodePipeline
    • C. CodeCommit
    • D. CodeBuild

    Answer: B.
    AWS CodePipeline is a fully managed continuous delivery service that helps you automate your release pipelines for fast and reliable application and infrastructure updates

    Reference: AWS CodePipeline

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    Q10: You want to use the output of your CloudFormation stack as input to another CloudFormation stack. Which sections of the CloudFormation template would you use to help you configure this?

    • A. Outputs
    • B. Transforms
    • C. Resources
    • D. Exports

    Answer: A.
    Outputs is used to output user defines data relating to the resources you have built and can also used as input to another CloudFormation stack.

    Reference: CloudFormation Outputs

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    Q11: You have some code located in an S3 bucket that you want to reference in your CloudFormation template. Which section of the template can you use to define this?

    • A. Inputs
    • B. Resources
    • C. Transforms
    • D. Files

    Answer: C.
    Transforms is used to reference code located in S3 and also specifying the use of the Serverless Application Model (SAM)
    for Lambda deployments.
    Transform:
    Name: 'AWS::Include'
    Parameters:
    Location: 's3://MyAmazonS3BucketName/MyFileName.yaml'

    Reference: Transforms

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    Q12: You are deploying an application to a number of Ec2 instances using CodeDeploy. What is the name of the file
    used to specify source files and lifecycle hooks?

    • A. buildspec.yml
    • B. appspec.json
    • C. appspec.yml
    • D. buildspec.json

    Answer: C.

    Reference: CodeDeploy AppSpec File Reference

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    Q13: Which of the following approaches allows you to re-use pieces of CloudFormation code in multiple templates, for common use cases like provisioning a load balancer or web server?

    • A. Share the code using an EBS volume
    • B. Copy and paste the code into the template each time you need to use it
    • C. Use a cloudformation nested stack
    • D. Store the code you want to re-use in an AMI and reference the AMI from within your CloudFormation template.

    Answer: C.

    Reference: Working with Nested Stacks

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    Q14: In the CodeDeploy AppSpec file, what are hooks used for?

    • A. To reference AWS resources that will be used during the deployment
    • B. Hooks are reserved for future use
    • C. To specify files you want to copy during the deployment.
    • D. To specify, scripts or function that you want to run at set points in the deployment lifecycle

    Answer: D.
    The 'hooks' section for an EC2/On-Premises deployment contains mappings that link deployment lifecycle event hooks to one or more scripts.

    Reference: AppSpec 'hooks' Section

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    Q15:You need to setup a RESTful API service in AWS that would be serviced via the following url https://democompany.com/customers Which of the following combination of services can be used for development and hosting of the RESTful service? Choose 2 answers from the options below

    • A. AWS Lambda and AWS API gateway
    • B. AWS S3 and Cloudfront
    • C. AWS EC2 and AWS Elastic Load Balancer
    • D. AWS SQS and Cloudfront

    Answer: A and C
    AWS Lambda can be used to host the code and the API gateway can be used to access the API’s which point to AWS Lambda Alternatively you can create your own API service , host it on an EC2 Instance and then use the AWS Application Load balancer to do path based routing.
    Reference: Build a Serverless Web Application with AWS Lambda, Amazon API Gateway, Amazon S3, Amazon DynamoDB, and Amazon Cognito

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    Q16: As a developer, you have created a Lambda function that is used to work with a bucket in Amazon S3. The Lambda function is not working as expected. You need to debug the issue and understand what’s the underlying issue. How can you accomplish this in an easily understandable way?

    • A. Use AWS Cloudwatch metrics
    • B. Put logging statements in your code
    • C. Set the Lambda function debugging level to verbose
    • D. Use AWS Cloudtrail logs

    Answer: B
    You can insert logging statements into your code to help you validate that your code is working as expected. Lambda automatically integrates with Amazon CloudWatch Logs and pushes all logs from your code to a CloudWatch Logs group associated with a Lambda function (/aws/lambda/).
    Reference: Using Amazon CloudWatch

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    Q17: You have a lambda function that is processed asynchronously. You need a way to check and debug issues if the function fails? How could you accomplish this?

    • A. Use AWS Cloudwatch metrics
    • B. Assign a dead letter queue
    • C. Congure SNS notications
    • D. Use AWS Cloudtrail logs

    Answer: B
    Any Lambda function invoked asynchronously is retried twice before the event is discarded. If the retries fail and you're unsure why, use Dead Letter Queues (DLQ) to direct unprocessed events to an Amazon SQS queue or an Amazon SNS topic to analyze the failure.
    Reference: AWS Lambda Function Dead Letter Queues

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    Q18: You are developing an application that is going to make use of Amazon Kinesis. Due to the high throughput , you decide to have multiple shards for the streams. Which of the following is TRUE when it comes to processing data across multiple shards?

    • A. You cannot guarantee the order of data across multiple shards. Its possible only within a shard
    • B. Order of data is possible across all shards in a streams
    • C. Order of data is not possible at all in Kinesis streams
    • D. You need to use Kinesis firehose to guarantee the order of data

    Answer: A
    Kinesis Data Streams lets you order records and read and replay records in the same order to many Kinesis Data Streams applications. To enable write ordering, Kinesis Data Streams expects you to call the PutRecord API to write serially to a shard while using the sequenceNumberForOrdering parameter. Setting this parameter guarantees strictly increasing sequence numbers for puts from the same client and to the same partition key.
    Option A is correct as it cannot guarantee the ordering of records across multiple shards.
    Reference: How to perform ordered data replication between applications by using Amazon DynamoDB Streams

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    Q19: You’ve developed a Lambda function and are now in the process of debugging it. You add the necessary print statements in the code to assist in the debugging. You go to Cloudwatch logs , but you see no logs for the lambda function. Which of the following could be the underlying issue for this?

    • A. You’ve not enabled versioning for the Lambda function
    • B. The IAM Role assigned to the Lambda function does not have the necessary permission to create Logs
    • C. There is not enough memory assigned to the function
    • D. There is not enough time assigned to the function

    Answer: B
    “If your Lambda function code is executing, but you don't see any log data being generated after several minutes, this could mean your execution role for the Lambda function did not grant permissions to write log data to CloudWatch Logs. For information about how to make sure that you have set up the execution role correctly to grant these permissions, see Manage Permissions: Using an IAM Role (Execution Role)”.

    Reference: Using Amazon CloudWatch

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    Q20: Your application is developed to pick up metrics from several servers and push them off to Cloudwatch. At times , the application gets client 429 errors. Which of the following can be done from the programming side to resolve such errors?

    • A. Use the AWS CLI instead of the SDK to push the metrics
    • B. Ensure that all metrics have a timestamp before sending them across
    • C. Use exponential backoff in your request
    • D. Enable encryption for the requests

    Answer: C.
    The main reason for such errors is that throttling is occurring when many requests are sent via API calls. The best way to mitigate this is to stagger the rate at which you make the API calls.
    In addition to simple retries, each AWS SDK implements exponential backoff algorithm for better flow control. The idea behind exponential backoff is to use progressively longer waits between retries for consecutive error responses. You should implement a maximum delay interval, as well as a maximum number of retries. The maximum delay interval and maximum number of retries are not necessarily fixed values and should be set based on the operation being performed, as well as other local factors, such as network latency.
    Reference: Error Retries and Exponential Backoff in AWS

    Q21: You have been instructed to use the CodePipeline service for the CI/CD automation in your company. Due to security reasons , the resources that would be part of the deployment are placed in another account. Which of the following steps need to be carried out to accomplish this deployment? Choose 2 answers from the options given below

    • A. Dene a customer master key in KMS
    • B. Create a reference Code Pipeline instance in the other account
    • C. Add a cross account role
    • D. Embed the access keys in the codepipeline process

    Answer: A. and C.
    You might want to create a pipeline that uses resources created or managed by another AWS account. For example, you might want to use one account for your pipeline and another for your AWS CodeDeploy resources. To do so, you must create a AWS Key Management Service (AWS KMS) key to use, add the key to the pipeline, and set up account policies and roles to enable cross-account access.
    Reference: Create a Pipeline in CodePipeline That Uses Resources from Another AWS Account

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    Q22: You are planning on deploying an application to the worker role in Elastic Beanstalk. Moreover, this worker application is going to run the periodic tasks. Which of the following is a must have as part of the deployment?

    • A. An appspec.yaml file
    • B. A cron.yaml  file
    • C. A cron.cong file
    • D. An appspec.json file

    Answer: B.
    Create an Application Source Bundle
    When you use the AWS Elastic Beanstalk console to deploy a new application or an application version, you'll need to upload a source bundle. Your source bundle must meet the following requirements:
    Consist of a single ZIP file or WAR file (you can include multiple WAR files inside your ZIP file)
    Not exceed 512 MB
    Not include a parent folder or top-level directory (subdirectories are fine)
    If you want to deploy a worker application that processes periodic background tasks, your application source bundle must also include a cron.yaml file. For more information, see Periodic Tasks.

    Reference: Create an Application Source Bundle

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    Q23: An application needs to make use of an SQS queue for working with messages. An SQS queue has been created with the default settings. The application needs 60 seconds to process each message. Which of the following step need to be carried out by the application.

    • A. Change the VisibilityTimeout for each message and then delete the message after processing is completed
    • B. Delete the message and change the visibility timeout.
    • C. Process the message , change the visibility timeout. Delete the message
    • D. Process the message and delete the message

    Answer: A
    If the SQS queue is created with the default settings , then the default visibility timeout is 30 seconds. And since the application needs more time for processing , you first need to change the timeout and delete the message after it is processed.
    Reference: Amazon SQS Visibility Timeout

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    Q24: AWS CodeDeploy deployment fails to start & generate following error code, ”HEALTH_CONSTRAINTS_INVALID”, Which of the following can be used to eliminate this error?

    • A. Make sure the minimum number of healthy instances is equal to the total number of instances in the deployment group.
    • B. Increase the number of healthy instances required during deployment
    • C. Reduce number of healthy instances required during deployment
    • D. Make sure the number of healthy instances is equal to the specified minimum number of healthy instances.

    Answer: C
    AWS CodeDeploy generates ”HEALTH_CONSTRAINTS_INVALID” error, when a minimum number of healthy instances defined in deployment group are not available during deployment. To mitigate this error, make sure required number of healthy instances are available during deployments.
    Reference: Error Codes for AWS CodeDeploy

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    Q25: How are the state machines in AWS Step Functions defined?

    • A. SAML
    • B. XML
    • C. YAML
    • D. JSON

    Answer: D. JSON
    AWS Step Functions state machines are defines in JSON files!
    Reference: What Is AWS Step Functions?

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    Q26:How can API Gateway methods be configured to respond to requests?

    • A. Forwarded to method handlers
    • B. AWS Lambda
    • C. Integrated with other AWS Services
    • D. Existing HTTP endpoints

    Answer: B. C. D.

    Reference: Set up REST API Methods in API Gateway

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    Q27: Which of the following could be an example of an API Gateway Resource URL for a trucks resource?

    • A. https://1a2sb3c4.execute-api.us-east-1.awsapigateway.com/trucks
    • B. https://trucks.1a2sb3c4.execute-api.us-east-1.amazonaws.com
    • C. https://1a2sb3c4.execute-api.amazonaws.com/trucks
    • D. https://1a2sb3c4.execute-api.us-east-1.amazonaws.com/cars

    Answer: C

    Reference: Amazon API Gateway Concepts

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    Q28: API Gateway Deployments are:

    • A. A specific snapshot of your API’s methods
    • B. A specific snapshot of all of your API’s settings, resources, and methods
    • C. A specific snapshot of your API’s resources
    • D. A specific snapshot of your API’s resources and methods

    Answer: D.
    AWS API Gateway Deployments are a snapshot of all the resources and methods of your API and their configuration.
    Reference: Deploying a REST API in Amazon API Gateway

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    Q29: A SWF workflow task or task execution can live up to how long?

    • A. 1 Year
    • B. 14 days
    • C. 24 hours
    • D. 3 days

    Answer: A. 1 Year
    Each workflow execution can run for a maximum of 1 year. Each workflow execution history can grow up to 25,000 events. If your use case requires you to go beyond these limits, you can use features Amazon SWF provides to continue executions and structure your applications using child workflow executions.
    Reference: Amazon SWF FAQs

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    Q30: With AWS Step Functions, all the work in your state machine is done by tasks. These tasks performs work by using what types of things? (Choose the best 3 answers)

    • A. An AWS Lambda Function Integration
    • B. Passing parameters to API actions of other services
    • C. Activities
    • D. An EC2 Integration

    Answer: A. B. C.

    Reference:

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    Q31: How does SWF make decisions?

    • A. A decider program that is written in the language of the developer’s choice
    • B. A visual workflow created in the SWF visual workflow editor
    • C. A JSON-defined state machine that contains states within it to select the next step to take
    • D. SWF outsources all decisions to human deciders through the AWS Mechanical Turk service.

    Answer: A.
    SWF allows the developer to write their own application logic to make decisions and determine how to evaluate incoming data.
    Q: What programming conveniences does Amazon SWF provide to write applications? Like other AWS services, Amazon SWF provides a core SDK for the web service APIs. Additionally, Amazon SWF offers an SDK called the AWS Flow Framework that enables you to develop Amazon SWF-based applications quickly and easily. AWS Flow Framework abstracts the details of task-level coordination with familiar programming constructs. While running your program, the framework makes calls to Amazon SWF, tracks your program’s execution state using the execution history kept by Amazon SWF, and invokes the relevant portions of your code at the right times. By offering an intuitive programming framework to access Amazon SWF, AWS Flow Framework enables developers to write entire applications as asynchronous interactions structured in a workflow. For more details, please see What is the AWS Flow Framework?
    Reference:

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    Q32: In order to effectively build and test your code, AWS CodeBuild allows you to:

    • A. Select and use some 3rd party providers to run tests against your code
    • B. Select a pre-configured environment
    • C. Provide your own custom AMI
    • D. Provide your own custom container image

    Answer:A. B. and D.

    Reference: AWS CodeBuild FAQs

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    Q33: X-Ray Filter Expressions allow you to search through request information using characteristics like:

    • A. URL Paths
    • B. Metadata
    • C. Trace ID
    • D. Annotations

    Top

    Q34: CodePipeline pipelines are workflows that deal with stages, actions, transitions, and artifacts. Which of the following statements is true about these concepts?

    • A. Stages contain at least two actions
    • B. Artifacts are never modified or iterated on when used inside of CodePipeline
    • C. Stages contain at least one action
    • D. Actions will have a deployment artifact as either an input an output or both

    Answer: B. C. D.

    Reference:

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    Q35: When deploying a simple Python web application with Elastic Beanstalk which of the following AWS resources will be created and managed for you by Elastic Beanstalk?

    • A. An Elastic Load Balancer
    • B. An S3 Bucket
    • C. A Lambda Function
    • D. An EC2 instance

    Answer: A. B. and D.
    AWS Elastic Beanstalk uses proven AWS features and services, such as Amazon EC2, Amazon RDS, Elastic Load Balancing, Auto Scaling, Amazon S3, and Amazon SNS, to create an environment that runs your application. The current version of AWS Elastic Beanstalk uses the Amazon Linux AMI or the Windows Server 2012 R2 AMI.
    Reference: AWS Elastic Beanstalk FAQs

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    Q36: Elastic Beanstalk is used to:

    • A. Deploy and scale web applications and services developed with a supported platform
    • B. Deploy and scale serverless applications
    • C. Deploy and scale applications based purely on EC2 instances
    • D. Manage the deployment of all AWS infrastructure resources of your AWS applications

    Answer: A.
    Who should use AWS Elastic Beanstalk?
    Those who want to deploy and manage their applications within minutes in the AWS Cloud. You don’t need experience with cloud computing to get started. AWS Elastic Beanstalk supports Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker web applications.
    Reference:

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    Q35: How can AWS X-Ray determine what data to collect?

    • A. X-Ray applies a sampling algorithm by default
    • B. X-Ray collects data on all requests by default
    • C. You can implement your own sampling frequencies for data collection
    • D. X-Ray collects data on all requests for services enabled with it

    Answer: A. and C.

    Reference: AWS X-Ray FAQs

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    Q37: Which API call is used to list all resources that belong to a CloudFormation Stack?

    • A. DescribeStacks
    • B. GetTemplate
    • C. DescribeStackResources
    • D. ListStackResources

    Answer: D.

    Reference: ListStackResources

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    Q38: What is the default behaviour of a CloudFormation stack if the creation of one resource fails?

    • A. Rollback
    • B. The stack continues creating and the failed resource is ignored
    • C. Delete
    • D. Undo

    Answer: A. Rollback

    Reference: AWS CloudFormation FAQs

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    Q39: Which AWS CLI command lists all current stacks in your CloudFormation service?

    • A. aws cloudformation describe-stacks
    • B. aws cloudformation list-stacks
    • C. aws cloudformation create-stack
    • D. aws cloudformation describe-stack-resources

    Answer: A. and B.

    Reference: list-stacks

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    Q40:
    Which API call is used to list all resources that belong to a CloudFormation Stack?

    • A. DescribeStacks
    • B. GetTemplate
    • C. ListStackResources
    • D. DescribeStackResources

    Answer: C.

    Reference: list-stack-resources

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    Q41: How does using ElastiCache help to improve database performance?

    • A. It can store petabytes of data
    • B. It provides faster internet speeds
    • C. It can store the results of frequent or highly-taxing queries
    • D. It uses read replicas

    Answer: C.
    With ElastiCache, customers get all of the benefits of a high-performance, in-memory cache with less of the administrative burden involved in launching and managing a distributed cache. The service makes setup, scaling, and cluster failure handling much simpler than in a self-managed cache deployment.
    Reference: Amazon ElastiCache

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    Q42: Which of the following best describes the Lazy Loading caching strategy?

    • A. Every time the underlying database is written to or updated the cache is updated with the new information.
    • B. Every miss to the cache is counted and when a specific number is reached a full copy of the database is migrated to the cache
    • C. A specific amount of time is set before the data in the cache is marked as expired. After expiration, a request for expired data will be made through to the backing database.
    • D. Data is added to the cache when a cache miss occurs (when there is no data in the cache and the request must go to the database for that data)

    Answer: D.
    Amazon ElastiCache is an in-memory key/value store that sits between your application and the data store (database) that it accesses. Whenever your application requests data, it first makes the request to the ElastiCache cache. If the data exists in the cache and is current, ElastiCache returns the data to your application. If the data does not exist in the cache, or the data in the cache has expired, your application requests the data from your data store which returns the data to your application. Your application then writes the data received from the store to the cache so it can be more quickly retrieved next time it is requested.
    Reference: Lazy Loading

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    Q43: What are two benefits of using RDS read replicas?

    • A. You can add/remove read replicas based on demand, so it creates elasticity for RDS.
    • B. Improves performance of the primary database by taking workload from it
    • C. Automatic failover in the case of Availability Zone service failures
    • D. Allows both reads and writes

    Answer: A. and B.

    Reference: Amazon RDS Read Replicas

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    Q44: What is the simplest way to enable an S3 bucket to be able to send messages to your SNS topic?

    • A. Attach an IAM role to the S3 bucket to send messages to SNS.
    • B. Activate the S3 pipeline feature to send notifications to another AWS service – in this case select SNS.
    • C. Add a resource-based access control policy on the SNS topic.
    • D. Use AWS Lambda to receive events from the S3 bucket and then use the Publish API action to send them to the SNS topic.

    Answer: C.

    Reference: Access Control List (ACL) Overview

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    Q45: You have just set up a push notification service to send a message to an app installed on a device with the Apple Push Notification Service. It seems to work fine. You now want to send a message to an app installed on devices for multiple platforms, those being the Apple Push Notification Service(APNS) and Google Cloud Messaging for Android (GCM). What do you need to do first for this to be successful?

    • A. Request Credentials from Mobile Platforms, so that each device has the correct access control policies to access the SNS publisher
    • B. Create a Platform Application Object which will connect all of the mobile devices with your app to the correct SNS topic.
    • C. Request a Token from Mobile Platforms, so that each device has the correct access control policies to access the SNS publisher.
    • D. Get a set of credentials in order to be able to connect to the push notification service you are trying to setup.

    Answer: D.
    To use Amazon SNS mobile push notifications, you need to establish a connection with a supported push notification service. This connection is established using a set of credentials.
    Reference: Add Device Tokens or Registration IDs

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    Q46: SNS message can be sent to different kinds of endpoints. Which of these is NOT currently a supported endpoint?

    • A. Slack Messages
    • B. SMS (text message)
    • C. HTTP/HTTPS
    • D. AWS Lambda

    Answer: A.
    Slack messages are not directly integrated with SNS, though theoretically, you could write a service to push messages to slack from SNS.
    Reference:

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    Q47: Company B provides an online image recognition service and utilizes SQS to decouple system components for scalability. The SQS consumers poll the imaging queue as often as possible to keep end-to-end throughput as high as possible. However, Company B is realizing that polling in tight loops is burning CPU cycles and increasing costs with empty responses. How can Company B reduce the number empty responses?

    • A. Set the imaging queue VisibilityTimeout attribute to 20 seconds
    • B. Set the imaging queue MessageRetentionPeriod attribute to 20 seconds
    • C. Set the imaging queue ReceiveMessageWaitTimeSeconds Attribute to 20 seconds
    • D. Set the DelaySeconds parameter of a message to 20 seconds

    Answer: C.
    Enabling long polling reduces the amount of false and empty responses from SQS service. It also reduces the number of calls that need to be made to a queue by staying connected to the queue until all messages have been received or until timeout. In order to enable long polling the ReceiveMessageWaitTimeSeconds attribute needs to be set to a number greater than 0. If it is set to 0 then short polling is enabled.
    Reference: Amazon SQS Long Polling

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    Q48: Which of the following statements about SQS standard queues are true?

    • A. Message order can be indeterminate – you’re not guaranteed to get messages in the same order they were sent in
    • B. Messages will be delivered exactly once and messages will be delivered in First in, First out order
    • C. Messages will be delivered exactly once and message delivery order is indeterminate
    • D. Messages can be delivered one or more times

    Answer: A. and D.
    A standard queue makes a best effort to preserve the order of messages, but more than one copy of a message might be delivered out of order. If your system requires that order be preserved, we recommend using a FIFO (First-In-First-Out) queue or adding sequencing information in each message so you can reorder the messages when they're received.
    Reference: Amazon SQS Standard Queues

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    Q49: Which of the following is true if long polling is enabled?

    • A. If long polling is enabled, then each poll only polls a subset of SQS servers; in order for all messages to be received, polling must continuously occur
    • B. The reader will listen to the queue until timeout
    • C. Increases costs because each request lasts longer
    • D. The reader will listen to the queue until a message is available or until timeout

    Answer: D.

    Reference: Amazon SQS Long Polling

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    Q50: When dealing with session state in EC2-based applications using Elastic load balancers which option is generally thought of as the best practice for managing user sessions?

    • A. Having the ELB distribute traffic to all EC2 instances and then having the instance check a caching solution like ElastiCache running Redis or Memcached for session information
    • B. Permanently assigning users to specific instances and always routing their traffic to those instances
    • C. Using Application-generated cookies to tie a user session to a particular instance for the cookie duration
    • D. Using Elastic Load Balancer generated cookies to tie a user session to a particular instance

    Answer: A.

    Reference: Distributed Session Management

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    Q51: When requested through an STS API call, credentials are returned with what three components?

    • A. Security Token, Access Key ID, Signed URL
    • B. Security Token, Access Key ID, Secret Access Key
    • C. Signed URL, Security Token, Username
    • D. Security Token, Secret Access Key, Personal Pin Code

    Answer: B.
    Security Token, Access Key ID, Secret Access Key
    Reference:

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    Q52: Your application must write to an SQS queue. Your corporate security policies require that AWS credentials are always encrypted and are rotated at least once a week.
    How can you securely provide credentials that allow your application to write to the queue?

    • A. Have the application fetch an access key from an Amazon S3 bucket at run time.
    • B. Launch the application’s Amazon EC2 instance with an IAM role.
    • C. Encrypt an access key in the application source code.
    • D. Enroll the instance in an Active Directory domain and use AD authentication.

    Answer: B.
    IAM roles are based on temporary security tokens, so they are rotated automatically. Keys in the source code cannot be rotated (and are a very bad idea). It’s impossible to retrieve credentials from an S3 bucket if you don’t already have credentials for that bucket. Active Directory authorization will not grant access to AWS resources.
    Reference: AWS IAM FAQs

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    Q53: Your web application reads an item from your DynamoDB table, changes an attribute, and then writes the item back to the table. You need to ensure that one process doesn’t overwrite a simultaneous change from another process.
    How can you ensure concurrency?

    • A. Implement optimistic concurrency by using a conditional write.
    • B. Implement pessimistic concurrency by using a conditional write.
    • C. Implement optimistic concurrency by locking the item upon read.
    • D. Implement pessimistic concurrency by locking the item upon read.

    Answer: A.
    Optimistic concurrency depends on checking a value upon save to ensure that it has not changed. Pessimistic concurrency prevents a value from changing by locking the item or row in the database. DynamoDB does not support item locking, and conditional writes are perfect for implementing optimistic concurrency.
    Reference: Optimistic Locking With Version Number

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    Q54: Which statements about DynamoDB are true? Choose 2 answers

    • A. DynamoDB uses optimistic concurrency control
    • B. DynamoDB restricts item access during writes
    • C. DynamoDB uses a pessimistic locking model
    • D. DynamoDB restricts item access during reads
    • E. DynamoDB uses conditional writes for consistency

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    Q55: Your CloudFormation template has the following Mappings section:

    Which JSON snippet will result in the value “ami-6411e20d” when a stack is launched in us-east-1?

    • A. { “Fn::FindInMap” : [ “Mappings”, { “RegionMap” : [“us-east-1”, “us-west-1”] }, “32”]}
    • B. { “Fn::FindInMap” : [ “Mappings”, { “Ref” : “AWS::Region” }, “32”]}
    • C. { “Fn::FindInMap” : [ “RegionMap”, { “Ref” : “AWS::Region” }, “32”]}
    • D. { “Fn::FindInMap” : [ “RegionMap”, { “RegionMap” : “AWS::Region” }, “32”]}

    Answer: C.
    The intrinsic function Fn::FindInMap returns the value corresponding to keys in a two-level map that is declared in the Mappings section.
    You can use the Fn::FindInMap function to return a named value based on a specified key. The following example template contains an Amazon EC2 resource whose ImageId property is assigned by the FindInMap function. The FindInMap function specifies key as the region where the stack is created (using the AWS::Region pseudo parameter) and HVM64 as the name of the value to map to.
    Reference:

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    Q56: Your application triggers events that must be delivered to all your partners. The exact partner list is constantly changing: some partners run a highly available endpoint, and other partners’ endpoints are online only a few hours each night. Your application is mission-critical, and communication with your partners must not introduce delay in its operation. A delay in delivering the event to one partner cannot delay delivery to other partners.

    What is an appropriate way to code this?

    • A. Implement an Amazon SWF task to deliver the message to each partner. Initiate an Amazon SWF workflow execution.
    • B. Send the event as an Amazon SNS message. Instruct your partners to create an HTTP. Subscribe their HTTP endpoint to the Amazon SNS topic.
    • C. Create one SQS queue per partner. Iterate through the queues and write the event to each one. Partners retrieve messages from their queue.
    • D. Send the event as an Amazon SNS message. Create one SQS queue per partner that subscribes to the Amazon SNS topic. Partners retrieve messages from their queue.

    Answer: D.
    There are two challenges here: the command must be “fanned out” to a variable pool of partners, and your app must be decoupled from the partners because they are not highly available.
    Sending the command as an SNS message achieves the fan-out via its publication/subscribe model, and using an SQS queue for each partner decouples your app from the partners. Writing the message to each queue directly would cause more latency for your app and would require your app to monitor which partners were active. It would be difficult to write an Amazon SWF workflow for a rapidly changing set of partners.

    Reference: AWS SNS Faqs

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    Q57: You have a three-tier web application (web, app, and data) in a single Amazon VPC. The web and app tiers each span two Availability Zones, are in separate subnets, and sit behind ELB Classic Load Balancers. The data tier is a Multi-AZ Amazon RDS MySQL database instance in database subnets.
    When you call the database tier from your app tier instances, you receive a timeout error. What could be causing this?

    • A. The IAM role associated with the app tier instances does not have rights to the MySQL database.
    • B. The security group for the Amazon RDS instance does not allow traffic on port 3306 from the app
      instances.
    • C. The Amazon RDS database instance does not have a public IP address.
    • D. There is no route defined between the app tier and the database tier in the Amazon VPC.

    Answer: B.
    Security groups block all network traffic by default, so if a group is not correctly configured, it can lead to a timeout error. MySQL security, not IAM, controls MySQL security. All subnets in an Amazon VPC have routes to all other subnets. Internal traffic within an Amazon VPC does not require public IP addresses.

    Reference: Security Groups for Your VPC

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    Q58: What type of block cipher does Amazon S3 offer for server side encryption?

    • A. RC5
    • B. Blowfish
    • C. Triple DES
    • D. Advanced Encryption Standard

    Answer: D
    Amazon S3 server-side encryption uses one of the strongest block ciphers available, 256-bit Advanced Encryption Standard (AES-256), to encrypt your data.

    Reference: Protecting Data Using Server-Side Encryption

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    Q59: You have written an application that uses the Elastic Load Balancing service to spread
    traffic to several web servers Your users complain that they are sometimes forced to login
    again in the middle of using your application, after they have already togged in. This is not
    behaviour you have designed. What is a possible solution to prevent this happening?

    • A. Use instance memory to save session state.
    • B. Use instance storage to save session state.
    • C. Use EBS to save session state
    • D. Use ElastiCache to save session state.
    • E. Use Glacier to save session slate.

    Answer: D.
    You can cache a variety of objects using the service, from the content in persistent data stores (such as Amazon RDS, DynamoDB, or self-managed databases hosted on EC2) to dynamically generated web pages (with Nginx for example), or transient session data that may not require a persistent backing store. You can also use it to implement high-frequency counters to deploy admission control in high volume web applications.

    Reference: Amazon ElastiCache FAQs

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    Q60: You are writing to a DynamoDB table and receive the following exception:”
    ProvisionedThroughputExceededException”. though according to your Cloudwatch metrics
    for the table, you are not exceeding your provisioned throughput. What could be an
    explanation for this?

    • A. You haven’t provisioned enough DynamoDB storage instances
    • B. You’re exceeding your capacity on a particular Range Key
    • C. You’re exceeding your capacity on a particular Hash Key
    • D. You’re exceeding your capacity on a particular Sort Key
    • E. You haven’t configured DynamoDB Auto Scaling triggers

    Answer: C.
    The primary key that uniquely identifies each item in a DynamoDB table can be simple (a partition key only) or composite (a partition key combined with a sort key).
    Generally speaking, you should design your application for uniform activity across all logical partition keys in the Table and its secondary indexes.
    You can determine the access patterns that your application requires, and estimate the total read capacity units and write capacity units that each table and secondary Index requires.

    As traffic starts to flow, DynamoDB automatically supports your access patterns using the throughput you have provisioned, as long as the traffic against a given partition key does not exceed 3000 read capacity units or 1000 write capacity units.

    Reference: Best Practices for Designing and Using Partition Keys Effectively

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    Q61: Which DynamoDB limits can be raised by contacting AWS support?

    • A. The number of hash keys per account
    • B. The maximum storage used per account
    • C. The number of tables per account
    • D. The number of local secondary indexes per account
    • E. The number of provisioned throughput units per account

    Answer: C. and E.

    For any AWS account, there is an initial limit of 256 tables per region.
    AWS places some default limits on the throughput you can provision.
    These are the limits unless you request a higher amount.
    To request a service limit increase see https://aws.amazon.com/support.Reference: Limits in DynamoDB

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    Q62: AWS CodeBuild allows you to compile your source code, run unit tests, and produce deployment artifacts by:

    • A. Allowing you to provide an Amazon Machine Image to take these actions within
    • B. Allowing you to select an Amazon Machine Image and provide a User Data bootstrapping script to prepare an instance to take these actions within
    • C. Allowing you to provide a container image to take these actions within
    • D. Allowing you to select from pre-configured environments to take these actions within

    Answer: C. and D.
    You can provide your own custom container image to build your deployment artifacts.
    You never actually pass a specific AMI to CodeBuild. Though you can provide a custom docker image which you could basically 'bootstrap' for the purposes of your build.
    Reference: AWS CodeBuild Faqs

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    Q63: Which of the following will not cause a CloudFormation stack deployment to rollback?

    • A. The template contains invalid JSON syntax
    • B. An AMI specified in the template exists in a different region than the one in which the stack is being deployed.
    • C. A subnet specified in the template does not exist
    • D. The template specifies an instance-store backed AMI and an incompatible EC2 instance type.

    Answer: A.
    Invalid JSON syntax will cause an error message during template validation. Until the syntax is fixed, the template will not be able to deploy resources, so there will not be a need to or opportunity to rollback.
    Reference: AWS CloudFormatio Faqs

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    Q64: Your team is using CodeDeploy to deploy an application which uses secure parameters that are stored in the AWS System Mangers Parameter Store. What two options below must be completed so CodeDeploy can deploy the application?

    • A. Use ssm get-parameters with –with-decryption option
    • B. Add permissions using AWS access keys
    • C. Add permissions using AWS IAM role
    • D. Use ssm get-parameters with –with-no-decryption option

    Answer: A. and C.

    Reference: Add permission using IAM role

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    Q65: A corporate web application is deployed within an Amazon VPC, and is connected to the corporate data center via IPSec VPN. The application must authenticate against the on-premise LDAP server. Once authenticated, logged-in users can only access an S3 keyspace specific to the user. Which of the solutions below meet these requirements? Choose two answers How would you authenticate to the application given these details? (Choose 2)

    • A. The application authenticates against LDAP, and retrieves the name of an IAM role associated with the user. The application then calls the IAM Security Token Service to assume that IAM Role. The application can use the temporary credentials to access the S3 keyspace.
    • B. Develop an identity broker which authenticates against LDAP, and then calls IAM Security Token Service to get IAM federated user credentials. The application calls the identity broker to get IAM federated user credentials with access to the appropriate S3 keyspace
    • C. Develop an identity broker which authenticates against IAM Security Token Service to assume an IAM Role to get temporary AWS security credentials. The application calls the identity broker to get AWS temporary security credentials with access to the app
    • D. The application authenticates against LDAP. The application then calls the IAM Security Service to login to IAM using the LDAP credentials. The application can use the IAM temporary credentials to access the appropriate S3 bucket.

    Answer: A. and B.
    The question clearly says “authenticate against LDAP”. Temporary credentials come from STS. Federated user credentials come from the identity broker.
    Reference: IAM faqs

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    Q66:
    A corporate web application is deployed within an Amazon VPC, and is connected to the corporate data center via IPSec VPN. The application must authenticate against the on-premise LDAP server. Once authenticated, logged-in users can only access an S3 keyspace specific to the user. Which of the solutions below meet these requirements? Choose two answers
    How would you authenticate to the application given these details? (Choose 2)

    • A. The application authenticates against LDAP, and retrieves the name of an IAM role associated with the user. The application then calls the IAM Security Token Service to assume that IAM Role. The application can use the temporary credentials to access the S3 keyspace.
    • B. Develop an identity broker which authenticates against LDAP, and then calls IAM Security Token Service to get IAM federated user credentials. The application calls the identity broker to get IAM federated user credentials with access to the appropriate S3 keyspace
    • C. Develop an identity broker which authenticates against IAM Security Token Service to assume an IAM Role to get temporary AWS security credentials. The application calls the identity broker to get AWS temporary security credentials with access to the app
    • D. The application authenticates against LDAP. The application then calls the IAM Security Service to login to IAM using the LDAP credentials. The application can use the IAM temporary credentials to access the appropriate S3 bucket.

    Answer: A. and B.
    The question clearly says “authenticate against LDAP”. Temporary credentials come from STS. Federated user credentials come from the identity broker.
    Reference: AWA STS Faqs

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    Q67: When users are signing in to your application using Cognito, what do you need to do to make sure if the user has compromised credentials, they must enter a new password?

    • A. Create a user pool in Cognito
    • B. Block use for “Compromised credential” in the Basic security section
    • C. Block use for “Compromised credential” in the Advanced security section
    • D. Use secure remote password

    Answer: A. and C.
    Amazon Cognito can detect if a user’s credentials (user name and password) have been compromised elsewhere. This can happen when users reuse credentials at more than one site, or when they use passwords that are easy to guess.

    From the Advanced security page in the Amazon Cognito console, you can choose whether to allow, or block the user if compromised credentials are detected. Blocking requires users to choose another password. Choosing Allow publishes all attempted uses of compromised credentials to Amazon CloudWatch. For more information, see Viewing Advanced Security Metrics.

    You can also choose whether Amazon Cognito checks for compromised credentials during sign-in, sign-up, and password changes.

    Note Currently, Amazon Cognito doesn't check for compromised credentials for sign-in operations with Secure Remote Password (SRP) flow, which doesn't send the password during sign-in. Sign-ins that use the AdminInitiateAuth API with ADMIN_NO_SRP_AUTH flow and the InitiateAuth API with USER_PASSWORD_AUTH flow are checked for compromised credentials.

    Reference: AWS Cognito

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    Q68: You work in a large enterprise that is currently evaluating options to migrate your 27 GB Subversion code base. Which of the following options is the best choice for your organization?

    • A. AWS CodeHost
    • B. AWS CodeCommit
    • C. AWS CodeStart
    • D. None of these

    Answer: D.
    None of these. While CodeCommit is a good option for git reponsitories it is not able to host Subversion source control.

    Reference: Migration to CodeCommit

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    Q69: You are on a development team and you need to migrate your Spring Application over to AWS. Your team is looking to build, modify, and test new versions of the application. What AWS services could help you migrate your app?

    • A. Elastic Beanstalk
    • B. SQS
    • C. Ec2
    • D. AWS CodeDeploy

    Answer: A. C. and D.
    Amazon EC2 can be used to deploy various applications to your AWS Infrastructure.
    AWS CodeDeploy is a deployment service that automates application deployments to Amazon EC2 instances, on-premises instances, or serverless Lambda functions.

    Reference: AWS Deployment Faqs

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    Q70:
    You are a developer responsible for managing a high volume API running in your company’s datacenter. You have been asked to implement a similar API, but one that has potentially higher volume. And you must do it in the most cost effective way, using as few services and components as possible. The API stores and fetches data from a key value store. Which services could you utilize in AWS?

    • A. DynamoDB
    • B. Lambda
    • C. API Gateway
    • D. EC2

    Answer: A. and C.
    NoSQL databases like DynamoDB are designed for key value usage. DynamoDB can also handle incredible volumes and is cost effective. AWS API Gateway makes it easy for developers to create, publish, maintain, monitor, and secure APIs.

    Reference: API Gateway Faqs

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    Q71: By default, what event occurs if your CloudFormation receives an error during creation?

    • A. DELETE_IN_PROGRESS
    • B. CREATION_IN_PROGRESS
    • C. DELETE_COMPLETE
    • D. ROLLBACK_IN_PROGRESS

    Answer: D.

    Reference: Check Status Code

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    Q72:
    AWS X-Ray was recently implemented inside of a service that you work on. Several weeks later, after a new marketing push, that service started seeing a large spike in traffic and you’ve been tasked with investigating a few issues that have started coming up but when you review the X-Ray data you can’t find enough information to draw conclusions so you decide to:

    • A. Start passing in the X-Amzn-Trace-Id: True HTTP header from your upstream requests
    • B. Refactor the service to include additional calls to the X-Ray API using an AWS SDK
    • C. Update the sampling algorithm to increase the sample rate and instrument X-Ray to collect more pertinent information
    • D. Update your application to use the custom API Gateway TRACE method to send in data

    Answer: C.
    This is a good way to solve the problem – by customizing the sampling so that you can get more relevant information.

    Reference: AWS X-Ray facts

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    Q74: X-Ray metadata:

    • A. Associates request data with a particular Trace-ID
    • B. Stores key-value pairs of any type that are not searchable
    • C. Collects at the service layer to provide information on the overall health of the system
    • D. Stores key-value pairs of searchable information

    Answer:AB.
    X-Ray metadata stores key-value pairs of any type that are not searchable.
    Reference: AWS X-Rays faqs

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    Q75: Which of the following is the right sequence that gets called in CodeDeploy when you use Lambda hooks in an EC2/On-Premise Deployment?

    • A. Before Install-AfterInstall-Validate Service-Application Start
    • B. Before Install-After-Install-Application Stop-Application Start
    • C. Before Install-Application Stop-Validate Service-Application Start
    • D. Application Stop-Before Install-After Install-Application Start

    Answer: D.
    In an in-place deployment, including the rollback of an in-place deployment, event hooks are run in the following order:

    Note An AWS Lambda hook is one Lambda function specified with a string on a new line after the name of the lifecycle event. Each hook is executed once per deployment. Following are descriptions of the lifecycle events where you can run a hook during an Amazon ECS deployment.

    BeforeInstall – Use to run tasks before the replacement task set is created. One target group is associated with the original task set. If an optional test listener is specified, it is associated with the original task set. A rollback is not possible at this point. AfterInstall – Use to run tasks after the replacement task set is created and one of the target groups is associated with it. If an optional test listener is specified, it is associated with the original task set. The results of a hook function at this lifecycle event can trigger a rollback. AfterAllowTestTraffic – Use to run tasks after the test listener serves traffic to the replacement task set. The results of a hook function at this point can trigger a rollback. BeforeAllowTraffic – Use to run tasks after the second target group is associated with the replacement task set, but before traffic is shifted to the replacement task set. The results of a hook function at this lifecycle event can trigger a rollback. AfterAllowTraffic – Use to run tasks after the second target group serves traffic to the replacement task set. The results of a hook function at this lifecycle event can trigger a rollback. Run Order of Hooks in an Amazon ECS Deployment

    In an Amazon ECS deployment, event hooks run in the following order:

    For in-place deployments, the six hooks related to blocking and allowing traffic apply only if you specify a Classic Load Balancer, Application Load Balancer, or Network Load Balancer from Elastic Load Balancing in the deployment group.Note The Start, DownloadBundle, Install, and End events in the deployment cannot be scripted, which is why they appear in gray in this diagram. However, you can edit the 'files' section of the AppSpec file to specify what's installed during the Install event.

    Reference: Appspec.yml specs

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    Q76:
    Describe the process of registering a mobile device with SNS push notification service using GCM.

    • A. Receive Registration ID and token for each mobile device. Then, register the mobile application with Amazon SNS, and pass the GCM token credentials to Amazon SNS
    • B. Pass device token to SNS to create mobile subscription endpoint for each mobile device, then request the device token from each mobile device. SNS then communicates on your behalf to the GCM service
    • C. None of these are correct
    • D. Submit GCM notification credentials to Amazon SNS, then receive the Registration ID for each mobile device. After that, pass the device token to SNS, and SNS then creates a mobile subscription endpoint for each device and communicates with the GCM service on your behalf

    Answer: D.
    When you first register an app and mobile device with a notification service, such as Apple Push Notification Service (APNS) and Google Cloud Messaging for Android (GCM), device tokens or registration IDs are returned from the notification service. When you add the device tokens or registration IDs to Amazon SNS, they are used with the PlatformApplicationArn API to create an endpoint for the app and device. When Amazon SNS creates the endpoint, an EndpointArn is returned. The EndpointArn is how Amazon SNS knows which app and mobile device to send the notification message to.

    Reference: AWS Mobile Push Send device token

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    Q77:
    You run an ad-supported photo sharing website using S3 to serve photos to visitors of your site. At some point you find out that other sites have been linking to the photos on your site, causing loss to your business. What is an effective method to mitigate this?

    • A. Store photos on an EBS volume of the web server.
    • B. Block the IPs of the offending websites in Security Groups.
    • C. Remove public read access and use signed URLs with expiry dates.
    • D. Use CloudFront distributions for static content.

    Answer: C.
    This solves the issue, but does require you to modify your website. Your website already uses S3, so it doesn't require a lot of changes. See the docs for details: http://docs.aws.amazon.com/AmazonS3/latest/dev/ShareObjectPreSignedURL.html

    Reference: AWS S3 shared objects presigned urls

    CloudFront on its own doesn't prevent unauthorized access and requires you to add a whole new layer to your stack (which may make sense anyway). You can serve private content, but you'd have to use signed URLs or similar mechanism. Here are the docs: http://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/PrivateContent.html

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    Q78: How can you control access to the API Gateway in your environment?

    • A. Cognito User Pools
    • B. Lambda Authorizers
    • C. API Methods
    • D. API Stages

    Answer: A. and B.
    Access to a REST API Using Amazon Cognito User Pools as Authorizer
    As an alternative to using IAM roles and policies or Lambda authorizers (formerly known as custom authorizers), you can use an Amazon Cognito user pool to control who can access your API in Amazon API Gateway.

    To use an Amazon Cognito user pool with your API, you must first create an authorizer of the COGNITO_USER_POOLS type and then configure an API method to use that authorizer. After the API is deployed, the client must first sign the user in to the user pool, obtain an identity or access token for the user, and then call the API method with one of the tokens, which are typically set to the request's Authorization header. The API call succeeds only if the required token is supplied and the supplied token is valid, otherwise, the client isn't authorized to make the call because the client did not have credentials that could be authorized.

    The identity token is used to authorize API calls based on identity claims of the signed-in user. The access token is used to authorize API calls based on the custom scopes of specified access-protected resources. For more information, see Using Tokens with User Pools and Resource Server and Custom Scopes.

    Reference: AWS API Gateway integrate with Cognito

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    Q79: What kind of message does SNS send to endpoints?

    • A. An XML document with parameters like Message, Source, Destination, Type
    • B. A JSON document with parameters like Message, Signature, Subject, Type.
    • C. An XML document with parameters like Message, Signature, Subject, Type
    • D. A JSON document with parameters like Message, Source, Destination, Type

    Answer: B.
    Amazon SNS messages do not publish the source/destination

    Reference: AWS SNS Faqs

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    Q80: Company B provides an online image recognition service and utilizes SQS to decouple system components for scalability. The SQS consumers poll the imaging queue as often as possible to keep end-to-end throughput as high as possible. However, Company B is realizing that polling in tight loops is burning CPU cycles and increasing costs with empty responses. How can Company B reduce the number of empty responses?

    • A. Set the imaging queue MessageRetentionPeriod attribute to 20 seconds.
    • B. Set the imaging queue ReceiveMessageWaitTimeSeconds attribute to 20 seconds.
    • C. Set the imaging queue VisibilityTimeout attribute to 20 seconds.
    • D. Set the DelaySeconds parameter of a message to 20 seconds.

    Answer: B.
    ReceiveMessageWaitTimeSeconds, when set to greater than zero, enables long polling. Long polling allows the Amazon SQS service to wait until a message is available in the queue before sending a response. Short polling continuously pools a queue and can have false positives. Enabling long polling reduces the number of poll requests, false positives, and empty responses.
    Reference: AWS SQS Long Polling

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    81: You’re using CloudFormation templates to build out staging environments. What section of the CloudFormation would you edit in order to allow the user to specify the PEM key-name at start time?

    • A. Resources Section
    • B. Parameters Section
    • C. Mappings Section
    • D. Declaration Section

    Answer:B.

    Parameters property type in CloudFormation allows you to accept user input when starting the CloudFormation template. It allows you to reference the user input as variable throughout your CloudFormation template. Other examples might include asking the user starting the template to provide Domain admin passwords, instance size, pem key, region, and other dynamic options.

    Reference: AWS CloudFormation Parameters

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    Q82: You are writing an AWS CloudFormation template and you want to assign values to properties that will not be available until runtime. You know that you can use intrinsic functions to do this but are unsure as to which part of the template they can be used in. Which of the following is correct in describing how you can currently use intrinsic functions in an AWS CloudFormation template?

    • A. You can use intrinsic functions in any part of a template, except AWSTemplateFormatVersion and Description
    • B. You can use intrinsic functions in any part of a template.
    • C. You can use intrinsic functions only in the resource properties part of a template.
    • D. You can only use intrinsic functions in specific parts of a template. You can use intrinsic functions in resource properties, metadata attributes, and update policy attributes.

    Answer: D.

    You can use intrinsic functions only in specific parts of a template. Currently, you can use intrinsic functions in resource properties, outputs, metadata attributes, and update policy attributes. You can also use intrinsic functions to conditionally create stack resources.Reference: AWS Intrinsic Functions

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DevOps: In IT world, DevOps means Development Operations. The DevOps is the bridge between the developers, the servers and the infrastructure and his main role is to automate the process of delivering code to operations.
DevOps on wikipedia: is a software development process that emphasizes communication and collaboration between product management, software development, and operations professionals. DevOps also automates the process of software integration, testing, deployment and infrastructure changes.[1][2] It aims to establish a culture and environment where building, testing, and releasing software can happen rapidly, frequently, and more reliably.

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Things to do to build more diverse technical teams?

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What are specific Things to do to build more diverse technical teams?

  • 1. Start at the junior high school level in poor neighborhood by helping schools to teach minority kids how to code.
  • 2. Help set up computer labs in junior high school in poor neighborhoods so that kids who cannot afford the equipment can use the lab.
  • 3. Start coding challenges with prizes in those high school neighborhood and make the kids feel confident about their abilities.
  • 4. Get successful minorities entrepreneurs to host the kids and give them tours of their companies and offices and projects to inspire them.
  • 5. Give internships to minorities kids starting in high school.
  • 6. For the kids in senior high school who missed early adoption to coding, speed them up with after hours coding classes to prepare them for college.
  • 7. In college, make the minority kids feel welcome. Try to make them part of all activities ( computer related or not).
  • 8. For the kids in senior high school who missed early adoption to coding, speed them up with after hours coding classes to prepare them for college.
  • 9. In college, make the minority kids feel welcome. Try to make them part of all activities ( computer related or not). Help find internships from top IT firms (Google, Apple, IBM, etc.) for minorities.
  • 10. When minorities start working after college, welcome them in the team and trust them. Don’t just invite certain kids to dinner and golf. It makes minorities feel unappreciated and unwanted.
  • 11. Give minorities the same opportunity of advancement at work with other employees.
  • 12. Inclusion, inclusion, inclusion in every aspect of the company is the key.
  • Assess the progress of your efforts and adjust. Make sure that team leads or managers are open minded people and make it a priority for them to build diverse teams, because in the long run, it will be a win win for the company

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