Tag: AWS Certified Data Analytics – Specialty Questions and Answers Dumps

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Question2: A company ingests a large set of clickstream data in nested JSON format from different sources and stores it in Amazon S3. Data analysts need to analyze this data in combination with data stored in an Amazon Redshift cluster. Data analysts want to build a cost-effective and automated solution for this need.
Which solution meets these requirements?

A) Use Apache Spark SQL on Amazon EMR to convert the clickstream data to a tabular format. Use the Amazon Redshift COPY command to load the data into the Amazon Redshift cluster.

B) Use AWS Lambda to convert the data to a tabular format and write it to Amazon S3. Use the Amazon Redshift COPY command to load the data into the Amazon Redshift cluster.

C) Use the Relationalize class in an AWS Glue ETL job to transform the data and write the data back to Amazon S3. Use Amazon Redshift Spectrum to create external tables and join with the internal tables.

D) Use the Amazon Redshift COPY command to move the clickstream data directly into new tables in the Amazon Redshift cluster.

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ANSWER2:

Notes/Hint2:

The Relationalize PySpark transform can be used to flatten the nested data into a structured format. Amazon Redshift Spectrum can join the external tables and query the transformed clickstream data in place rather than needing to scale the cluster to accommodate the large dataset.

Reference1: Relationalize PySpark

Question 3: There is a five-day car rally race across Europe. The race coordinators are using a Kinesis stream and IoT sensors to monitor the movement of the cars. Each car has a sensor and data is getting back to the stream with the default stream settings. On the last day of the rally, data is sent to S3. When you go to interpret the data in S3, there is only data for the last day and nothing for the first 4 days. Which of the following is the most probable cause of this?

A) You did not have versioning enabled and would need to create individual buckets to prevent the data from being overwritten.

B) Data records are only accessible for a default of 24 hours from the time they are added to a stream.

C) One of the sensors failed, so there was no data to record.

D) You needed to use EMR to send the data to S3; Kinesis Streams are only compatible with DynamoDB.

ANSWER3:

Notes/Hint3:

Streams support changes to the data record retention period of your stream. An Amazon Kinesis stream is an ordered sequence of data records, meant to be written to and read from in real-time. Data records are therefore stored in shards in your stream temporarily. The period from when a record is added to when it is no longer accessible is called the retention period. An Amazon Kinesis stream stores records for 24 hours by default, up to 168 hours.

Reference3: Kinesis Extended Reading

Question 4: A publisher website captures user activity and sends clickstream data to Amazon Kinesis Data Streams. The publisher wants to design a cost-effective solution to process the data to create a timeline of user activity within a session. The solution must be able to scale depending on the number of active sessions.
Which solution meets these requirements?

A) Include a variable in the clickstream data from the publisher website to maintain a counter for the number of active user sessions. Use a timestamp for the partition key for the stream. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the counter. Deploy the consumer application on Amazon EC2 instances in an EC2 Auto Scaling group.

B) Include a variable in the clickstream to maintain a counter for each user action during their session. Use the action type as the partition key for the stream. Use the Kinesis Client Library (KCL) in the consumer application to retrieve the data from the stream and perform the processing. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the
counter. Deploy the consumer application on AWS Lambda.

C) Include a session identifier in the clickstream data from the publisher website and use as the partition key for the stream. Use the Kinesis Client Library (KCL) in the consumer application to retrieve the data from the stream and perform the processing. Deploy the consumer application on Amazon EC2 instances in an
EC2 Auto Scaling group. Use an AWS Lambda function to reshard the stream based upon Amazon CloudWatch alarms.

D) Include a variable in the clickstream data from the publisher website to maintain a counter for the number of active user sessions. Use a timestamp for the partition key for the stream. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the counter. Deploy the consumer application on AWS Lambda.

ANSWER4:

Notes/Hint4:

Partitioning by the session ID will allow a single processor to process all the actions for a user session in order. An AWS Lambda function can call the UpdateShardCount API action to change the number of shards in the stream. The KCL will automatically manage the number of processors to match the number of shards. Amazon EC2 Auto Scaling will assure the correct number of instances are running to meet the processing load.

Reference4: UpdateShardCount API

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Question 5: Your company has two batch processing applications that consume financial data about the day’s stock transactions. Each transaction needs to be stored durably and guarantee that a record of each application is delivered so the audit and billing batch processing applications can process the data. However, the two applications run separately and several hours apart and need access to the same transaction information. After reviewing the transaction information for the day, the information no longer needs to be stored. What is the best way to architect this application?

A) Use SQS for storing the transaction messages; when the billing batch process performs first and consumes the message, write the code in a way that does not remove the message after consumed, so it is available for the audit application several hours later. The audit application can consume the SQS message and remove it from the queue when completed.

B) Use Kinesis to store the transaction information. The billing application will consume data from the stream and the audit application can consume the same data several hours later.

C) Store the transaction information in a DynamoDB table. The billing application can read the rows while the audit application will read the rows then remove the data.

D) Use SQS for storing the transaction messages. When the billing batch process consumes each message, have the application create an identical message and place it in a different SQS for the audit application to use several hours later.

SQS would make this more difficult because the data does not need to persist after a full day.

ANSWER5:

Notes/Hint5:

Kinesis appears to be the best solution that allows multiple consumers to easily interact with the records.

Reference5: Amazon Kinesis

Question 6: A company is currently using Amazon DynamoDB as the database for a user support application. The company is developing a new version of the application that will store a PDF file for each support case ranging in size from 1–10 MB. The file should be retrievable whenever the case is accessed in the application.
How can the company store the file in the MOST cost-effective manner?

A) Store the file in Amazon DocumentDB and the document ID as an attribute in the DynamoDB table.

B) Store the file in Amazon S3 and the object key as an attribute in the DynamoDB table.

C) Split the file into smaller parts and store the parts as multiple items in a separate DynamoDB table.

D) Store the file as an attribute in the DynamoDB table using Base64 encoding.

ANSWER6:

Notes/Hint6:

Use Amazon S3 to store large attribute values that cannot fit in an Amazon DynamoDB item. Store each file as an object in Amazon S3 and then store the object path in the DynamoDB item.

Reference6: S3 Storage Cost – DynamODB Storage Cost

Question 7: Your client has a web app that emits multiple events to Amazon Kinesis Streams for reporting purposes. Critical events need to be immediately captured before processing can continue, but informational events do not need to delay processing. What solution should your client use to record these types of events without unnecessarily slowing the application?

A) Log all events using the Kinesis Producer Library.

B) Log critical events using the Kinesis Producer Library, and log informational events using the PutRecords API method.

C) Log critical events using the PutRecords API method, and log informational events using the Kinesis Producer Library.

D) Log all events using the PutRecords API method.

ANSWER2:

Notes/Hint7:

The PutRecords API can be used in code to be synchronous; it will wait for the API request to complete before the application continues. This means you can use it when you need to wait for the critical events to finish logging before continuing. The Kinesis Producer Library is asynchronous and can send many messages without needing to slow down your application. This makes the KPL ideal for the sending of many non-critical alerts asynchronously.

Reference7: PutRecords API

Question 8: You work for a start-up that tracks commercial delivery trucks via GPS. You receive coordinates that are transmitted from each delivery truck once every 6 seconds. You need to process these coordinates in near real-time from multiple sources and load them into Elasticsearch without significant technical overhead to maintain. Which tool should you use to digest the data?

A) Amazon SQS

B) Amazon EMR

C) AWS Data Pipeline

D) Amazon Kinesis Firehose

ANSWER8:

Notes/Hint8:

Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service, enabling near real-time analytics with existing business intelligence tools and dashboards.

Reference8: Amazon Kinesis Firehose

Question 9: A company needs to implement a near-real-time fraud prevention feature for its ecommerce site. User and order details need to be delivered to an Amazon SageMaker endpoint to flag suspected fraud. The amount of input data needed for the inference could be as much as 1.5 MB.
Which solution meets the requirements with the LOWEST overall latency?

A) Create an Amazon Managed Streaming for Kafka cluster and ingest the data for each order into a topic. Use a Kafka consumer running on Amazon EC2 instances to read these messages and invoke the Amazon SageMaker endpoint.

B) Create an Amazon Kinesis Data Streams stream and ingest the data for each order into the stream. Create an AWS Lambda function to read these messages and invoke the Amazon SageMaker endpoint.

C) Create an Amazon Kinesis Data Firehose delivery stream and ingest the data for each order into the stream. Configure Kinesis Data Firehose to deliver the data to an Amazon S3 bucket. Trigger an AWS Lambda function with an S3 event notification to read the data and invoke the Amazon SageMaker endpoint.

D) Create an Amazon SNS topic and publish the data for each order to the topic. Subscribe the Amazon SageMaker endpoint to the SNS topic.

ANSWER9:

Notes/Hint9:

An Amazon Managed Streaming for Kafka cluster can be used to deliver the messages with very low latency. It has a configurable message size that can handle the 1.5 MB payload.

Reference9: Amazon Managed Streaming for Kafka cluster

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Question 10: You need to filter and transform incoming messages coming from a smart sensor you have connected with AWS. Once messages are received, you need to store them as time series data in DynamoDB. Which AWS service can you use?

A) IoT Device Shadow Service

B) Redshift

C) Kinesis

D) IoT Rules Engine

ANSWER10:

Notes/Hint10:

The IoT rules engine will allow you to send sensor data over to AWS services like DynamoDB

Reference10: The IoT rules engine

Question 11: A media company is migrating its on-premises legacy Hadoop cluster with its associated data processing scripts and workflow to an Amazon EMR environment running the latest Hadoop release. The developers want to reuse the Java code that was written for data processing jobs for the on-premises cluster.
Which approach meets these requirements?

A) Deploy the existing Oracle Java Archive as a custom bootstrap action and run the job on the EMR cluster.

B) Compile the Java program for the desired Hadoop version and run it using a CUSTOM_JAR step on the EMR cluster.

C) Submit the Java program as an Apache Hive or Apache Spark step for the EMR cluster.

D) Use SSH to connect the master node of the EMR cluster and submit the Java program using the AWS CLI.

ANSWER11:

Notes/Hint11:

A CUSTOM JAR step can be configured to download a JAR file from an Amazon S3 bucket and execute it. Since the Hadoop versions are different, the Java application has to be recompiled.

Reference11: Automating analytics workflows on EMR

Question 12: You currently have databases running on-site and in another data center off-site. What service allows you to consolidate to one database in Amazon?

A) AWS Kinesis

B) AWS Database Migration Service

C) AWS Data Pipeline

D) AWS RDS Aurora

ANSWER12:

Notes/Hint12:

AWS Database Migration Service can migrate your data to and from most of the widely used commercial and open source databases. It supports homogeneous migrations such as Oracle to Oracle, as well as heterogeneous migrations between different database platforms, such as Oracle to Amazon Aurora. Migrations can be from on-premises databases to Amazon RDS or Amazon EC2, databases running on EC2 to RDS, or vice versa, as well as from one RDS database to another RDS database.

Reference12: DMS

Question 13: An online retail company wants to perform analytics on data in large Amazon S3 objects using Amazon EMR. An Apache Spark job repeatedly queries the same data to populate an analytics dashboard. The analytics team wants to minimize the time to load the data and create the dashboard.
Which approaches could improve the performance? (Select TWO.)

A) Copy the source data into Amazon Redshift and rewrite the Apache Spark code to create analytical reports by querying Amazon Redshift.

B) Copy the source data from Amazon S3 into Hadoop Distributed File System (HDFS) using s3distcp.

C) Load the data into Spark DataFrames.

D) Stream the data into Amazon Kinesis and use the Kinesis Connector Library (KCL) in multiple Spark jobs to perform analytical jobs.

E) Use Amazon S3 Select to retrieve the data necessary for the dashboards from the S3 objects.

ANSWER13:

C and E

Notes/Hint13:

One of the speed advantages of Apache Spark comes from loading data into immutable dataframes, which can be accessed repeatedly in memory. Spark DataFrames organizes distributed data into columns. This makes summaries and aggregates much quicker to calculate. Also, instead of loading an entire large Amazon S3 object, load only what is needed using Amazon S3 Select. Keeping the data in Amazon S3 avoids loading the large dataset into HDFS.

Reference13: Spark DataFrames

Question 14: You have been hired as a consultant to provide a solution to integrate a client’s on-premises data center to AWS. The customer requires a 300 Mbps dedicated, private connection to their VPC. Which AWS tool do you need?

A) VPC peering

B) Data Pipeline

C) Direct Connect

D) EMR

ANSWER14:

Notes/Hint14:

Direct Connect will provide a dedicated and private connection to an AWS VPC.

Reference14: Direct Connect

Question 15: Your organization has a variety of different services deployed on EC2 and needs to efficiently send application logs over to a central system for processing and analysis. They’ve determined it is best to use a managed AWS service to transfer their data from the EC2 instances into Amazon S3 and they’ve decided to use a solution that will do what?

A) Installs the AWS Direct Connect client on all EC2 instances and uses it to stream the data directly to S3.

B) Leverages the Kinesis Agent to send data to Kinesis Data Streams and output that data in S3.

C) Ingests the data directly from S3 by configuring regular Amazon Snowball transactions.

D) Leverages the Kinesis Agent to send data to Kinesis Firehose and output that data in S3.

ANSWER15:

Notes/Hint15:

Kinesis Firehose is a managed solution, and log files can be sent from EC2 to Firehose to S3 using the Kinesis agent.

Reference15: Kinesis Firehose

Question 16: A data engineer needs to create a dashboard to display social media trends during the last hour of a large company event. The dashboard needs to display the associated metrics with a latency of less than 1 minute.
Which solution meets these requirements?

A) Publish the raw social media data to an Amazon Kinesis Data Firehose delivery stream. Use Kinesis Data Analytics for SQL Applications to perform a sliding window analysis to compute the metrics and output the results to a Kinesis Data Streams data stream. Configure an AWS Lambda function to save the stream data to an Amazon DynamoDB table. Deploy a real-time dashboard hosted in an Amazon S3 bucket to read and display the metrics data stored in the DynamoDB table.

B) Publish the raw social media data to an Amazon Kinesis Data Firehose delivery stream. Configure the stream to deliver the data to an Amazon Elasticsearch Service cluster with a buffer interval of 0 seconds. Use Kibana to perform the analysis and display the results.

C) Publish the raw social media data to an Amazon Kinesis Data Streams data stream. Configure an AWS Lambda function to compute the metrics on the stream data and save the results in an Amazon S3 bucket. Configure a dashboard in Amazon QuickSight to query the data using Amazon Athena and display the results.

D) Publish the raw social media data to an Amazon SNS topic. Subscribe an Amazon SQS queue to the topic. Configure Amazon EC2 instances as workers to poll the queue, compute the metrics, and save the results to an Amazon Aurora MySQL database. Configure a dashboard in Amazon QuickSight to query the data in Aurora and display the results.

ANSWER16:

Notes/Hint16:

Amazon Kinesis Data Analytics can query data in a Kinesis Data Firehose delivery stream in near-real time using SQL. A sliding window analysis is appropriate for determining trends in the stream. Amazon S3 can host a static webpage that includes JavaScript that reads the data in Amazon DynamoDB and refreshes the dashboard.

Reference16: Amazon Kinesis Data Analytics can query data in a Kinesis Data Firehose delivery stream in near-real time using SQL

Question 17: A real estate company is receiving new property listing data from its agents through .csv files every day and storing these files in Amazon S3. The data analytics team created an Amazon QuickSight visualization report that uses a dataset imported from the S3 files. The data analytics team wants the visualization report to reflect the current data up to the previous day. How can a data analyst meet these requirements?

A) Schedule an AWS Lambda function to drop and re-create the dataset daily.

B) Configure the visualization to query the data in Amazon S3 directly without loading the data into SPICE.

C) Schedule the dataset to refresh daily.

D) Close and open the Amazon QuickSight visualization.

ANSWER17:

Notes/Hint17:

Datasets created using Amazon S3 as the data source are automatically imported into SPICE. The Amazon QuickSight console allows for the refresh of SPICE data on a schedule.

Reference17: Amazon QuickSight and SPICE

Question 18: You need to migrate data to AWS. It is estimated that the data transfer will take over a month via the current AWS Direct Connect connection your company has set up. Which AWS tool should you use?

A) Establish additional Direct Connect connections.

B) Use Data Pipeline to migrate the data in bulk to S3.

C) Use Kinesis Firehose to stream all new and existing data into S3.

D) Snowball

ANSWER18:

Notes/Hint18:

As a general rule, if it takes more than one week to upload your data to AWS using the spare capacity of your existing Internet connection, then you should consider using Snowball. For example, if you have a 100 Mb connection that you can solely dedicate to transferring your data and need to transfer 100 TB of data, it takes more than 100 days to complete a data transfer over that connection. You can make the same transfer by using multiple Snowballs in about a week.

Reference18: Snowball

Question 19: You currently have an on-premises Oracle database and have decided to leverage AWS and use Aurora. You need to do this as quickly as possible. How do you achieve this?

A) It is not possible to migrate an on-premises database to AWS at this time.

B) Use AWS Data Pipeline to create a target database, migrate the database schema, set up the data replication process, initiate the full load and a subsequent change data capture and apply, and conclude with a switchover of your production environment to the new database once the target database is caught up with the source database.

C) Use AWS Database Migration Services and create a target database, migrate the database schema, set up the data replication process, initiate the full load and a subsequent change data capture and apply, and conclude with a switch-over of your production environment to the new database once the target database is caught up with the source database.

D) Use AWS Glue to crawl the on-premises database schemas and then migrate them into AWS with Data Pipeline jobs.

https://aws.amazon.com/dms/faqs/

ANSWER19:

Notes/Hint19:

DMS can efficiently support this sort of migration using the steps outlined. While AWS Glue can help you crawl schemas and store metadata on them inside of Glue for later use, it isn’t the best tool for actually transitioning a database over to AWS itself. Similarly, while Data Pipeline is great for ETL and ELT jobs, it isn’t the best option to migrate a database over to AWS.

Reference19: DMS

Question 20: A financial company uses Amazon EMR for its analytics workloads. During the company’s annual security audit, the security team determined that none of the EMR clusters’ root volumes are encrypted. The security team recommends the company encrypt its EMR clusters’ root volume as soon as possible.
Which solution would meet these requirements?

A) Enable at-rest encryption for EMR File System (EMRFS) data in Amazon S3 in a security configuration. Re-create the cluster using the newly created security configuration.

B) Specify local disk encryption in a security configuration. Re-create the cluster using the newly created security configuration.

C) Detach the Amazon EBS volumes from the master node. Encrypt the EBS volume and attach it back to the master node.

D) Re-create the EMR cluster with LZO encryption enabled on all volumes.

ANSWER20:

Notes/Hint20:

Local disk encryption can be enabled as part of a security configuration to encrypt root and storage volumes.

Reference20: EMR Cluster Local disk encryption

Question 21: A company has a clickstream analytics solution using Amazon Elasticsearch Service. The solution ingests 2 TB of data from Amazon Kinesis Data Firehose and stores the latest data collected within 24 hours in an Amazon ES cluster. The cluster is running on a single index that has 12 data nodes and 3 dedicated master nodes. The cluster is configured with 3,000 shards and each node has 3 TB of EBS storage attached. The Data Analyst noticed that the query performance of Elasticsearch is sluggish, and some intermittent errors are produced by the Kinesis Data Firehose when it tries to write to the index. Upon further investigation, there were occasional JVMMemoryPressure errors found in Amazon ES logs.

What should be done to improve the performance of the Amazon Elasticsearch Service cluster?

A) Improve the cluster performance by increasing the number of master nodes of Amazon Elasticsearch.

B) Improve the cluster performance by increasing the number of shards of the Amazon Elasticsearch index.

C) Improve the cluster performance by decreasing the number of data nodes of Amazon Elasticsearch.

D) Improve the cluster performance by decreasing the number of shards of the Amazon Elasticsearch index.

ANSWER21:
D

Notes/Hint21:
“Amazon Elasticsearch Service (Amazon ES) is a managed service that makes it easy to deploy, operate, and scale Elasticsearch clusters in AWS Cloud. Elasticsearch is a popular open-source search and analytics engine for use cases such as log analytics, real-time application monitoring, and clickstream analysis. With Amazon ES, you get direct access to the Elasticsearch APIs; existing code and applications work seamlessly with the service.

Each Elasticsearch index is split into some number of shards. You should decide the shard count before indexing your first document. The overarching goal of choosing a number of shards is to distribute an index evenly across all data nodes in the cluster. However, these shards shouldn’t be too large or too numerous.

A good rule of thumb is to try to keep a shard size between 10 – 50 GiB. Large shards can make it difficult for Elasticsearch to recover from failure, but because each shard uses some amount of CPU and memory, having too many small shards can cause performance issues and out of memory errors. In other words, shards should be small enough that the underlying Amazon ES instance can handle them, but not so small that they place needless strain on the hardware. Therefore the correct answer is: Improve the cluster performance by decreasing the number of shards of Amazon Elasticsearch index.

Reference: ElasticsSearch

Question 22: A data lake is a central repository that enables which operation?

A) Store unstructured data from a single data source

B) Store structured data from any data source

C) Store structure and unstructured data from any source

D) Store structured and unstructured data from a single source

ANSWER22:
C

Notes/Hint22:
Data lake is a centralized repository for large amounts of structured and unstructured data to enable direct analytics.

Reference: Data Lakes

Question 23: What is the most cost-effective storage option for your data lake?

A) Amazon EBS

B) Amazon S3

C) Amazon RDS

D) Amazon Redshift

ANSWER23:
B

Notes/Hint23:
Amazon S3

Reference: Data Lakes – S3

Question 24: Which services are used in the processing layer of a data lake architecture? (SELECT TWO)

A. AWS Snowball

B. AWS Glue

C. Amazon EMR

D. Amazon QuickSight

ANSWER24:
B and C

Notes/Hint24:
Amazon Glue and Amazon EMR

Reference: Data Lakes – Glue and EMR

Question 25: Which services can be used for data ingestion into your data lake? (SELECT TWO)

A) Amazon Kinesis Data Firehose

B) Amazon QuickSight

C) Amazon Athena

D) AWS Storage Gateway

ANSWER25:
A and D

Notes/Hint25:
Amazon Kinesis Data Firehose and and Amazon Storage Gateway

Reference: Data Lakes

Question 26: Which service uses continuous data replication with high availability to consolidate databases into a petabyte-scale data warehouse by streaming data to amazon Redshift and Amazon S3?

A) AWS Storage Gateway

B) AWS Schema Conversion Tool

C) AWS Database Migration Service

D) Amazon Kinesis Data Firehose

ANSWER26:
C

Notes/Hint26:
AWS Database Migration Service

Reference: Data Lakes

Question 27: What is the AWS Glue Data Catalog?

A) A fully managed ETL (extract, transform, and load) pipeline service

B) A service to schedule jobs

C) A visual data preparation tool

D) An index to the location, schema, and runtime metrics of your data

ANSWER27:
D

Notes/Hint27:
An index to the location, schema, and runtime metrics of your data

Reference: Data Lakes

Questions 28: What AWS Glue feature “catalogs” your data?

A) AWS Glue crawler

B) AWS Glue DataBrew

C) AWS Glue Studio

D) AWS Glue Elastic Views

ANSWER28:
A

Notes/Hint28:
AWS Glue crawler

Reference: Data Lakes

Question 29: During your data preparation stage, the raw data has been enriched to support additional insights. You need to improve query performance and reduce costs of the final analytics solution.

Which data formats meet these requirements (SELECT TWO)

ANSWER29:
C and D

Notes/Hint29:
Apache Parquet and Apache ORC
Reference: Data Lakes

Question 30: Your small start-uo company is developing a data analytics solution. You need to clean and normalize large datasets, but you do not have developers with the skill set to write custom scripts. Which tool will help efficiently design and run the data preparation activities?

ANSWER30:
B

Notes/Hint30:
AWS Glue DataBrew
To be able to run analytics, build reports, or apply machine learning, you need to be sure the data you’re using is clean and in the right format. This data preparation step requires data analysts and data scientists to write custom code and perform many manual activities. When cleaning and normalizing data, it is helpful to first review the dataset to understand which possible values are present. Simple visualizations are helpful for determining whether correlations exist between the columns.

AWS Glue DataBrew is a visual data preparation tool that helps you clean and normalize data up to 80% faster so you can focus more on the business value you can get. DataBrew provides a visual interface that quickly connects to your data stored in Amazon S3, Amazon Redshift, Amazon Relational Database Service (RDS), any JDBC-accessible data store, or data indexed by the AWS Glue Data Catalog. You can then explore the data, look for patterns, and apply transformations. For example, you can apply joins and pivots, merge different datasets, or use functions to manipulate data.
Reference: Data Lakes

Question 30: In which scenario would you use AWS Glue jobs?

A) Analyze data in real-time as data comes into the data lake

B) Transform data in real-time as data comes into the data lake

C) Analyze data in batches on schedule or on demand

D) Transform data in batches on schedule or on demand.

ANSWER30:
D

Notes/Hint30:

An AWS Glue job encapsulates a script that connects to your source data, processes it, and then writes it out to your data target. Typically, a job runs extract, transform, and load (ETL) scripts. Jobs can also run general-purpose Python scripts (Python shell jobs.) AWS Glue triggers can start jobs based on a schedule or event, or on demand. You can monitor job runs to understand runtime metrics such as completion status, duration, and start tim

Question 31: Your data resides in multiple data stores, including Amazon S3, Amazon RDS, and Amazon DynamoDB. You need to efficiently query the combined datasets.

Which tool can achieve this, using a single query, without moving data?

A) Amazon Athena Federated Query

B) Amazon Redshift Query Editor

C) SQl Workbench

D) AWS Glue DataBrew

ANSWER31:
A

Notes/Hint31:
With Amazon Athena Federated Query, you can run SQL queries across a variety of relational, non-relational, and custom data sources. You get a unified way to run SQL queries across various data stores.

Athena uses data source connectors that run on AWS Lambda to run federated queries. A data source connector is a piece of code that can translate between your target data source and Athena. You can think of a connector as an extension of Athena’s query engine. Pre-built Athena data source connectors exist for data sources like Amazon CloudWatch Logs, Amazon DynamoDB, Amazon DocumentDB, Amazon RDS, and JDBC-compliant relational data sources such MySQL and PostgreSQL under the Apache 2.0 license. You can also use the Athena Query Federation SDK to write custom connectors. To choose, configure, and deploy a data source connector to your account, you can use the Athena and Lambda consoles or the AWS Serverless Application Repository. After you deploy data source connectors, the connector is associated with a catalog that you can specify in SQL queries. You can combine SQL statements from multiple catalogs and span multiple data sources with a single query.

Question 32: Which benefit do you achieve by using AWS Lake Formation to build data lakes?

A) Build data lakes quickly

B) Simplify security management

C) Provide self-service access to data

D) All of the above

ANSWER32:
D

Notes/Hint32:
Build data lakes quickly
With Lake Formation, you can move, store, catalog, and clean your data faster. You simply point Lake Formation at your data sources, and Lake Formation crawls those sources and moves the data into your new Amazon S3 data lake. Lake Formation organizes data in S3 around frequently used query terms and into right-sized chunks to increase efficiency. Lake Formation also changes data into formats like Apache Parquet and ORC for faster analytics. In addition, Lake Formation has built-in machine learning to deduplicate and find matching records (two entries that refer to the same thing) to increase data quality.

Simplify security management
You can use Lake Formation to centrally define security, governance, and auditing policies in one place, versus doing these tasks per service. You can then enforce those policies for your users across their analytics applications. Your policies are consistently implemented, eliminating the need to manually configure them across security services like AWS Identity and Access Management (AWS IAM) and AWS Key Management Service (AWS KMS), storage services like Amazon S3, and analytics and machine learning services like Amazon Redshift, Amazon Athena, and (in beta) Amazon EMR for Apache Spark. This reduces the effort in configuring policies across services and provides consistent enforcement and compliance.

Provide self-service access to data

With Lake Formation, you build a data catalog that describes the different available datasets along with which groups of users have access to each. This makes your users more productive by helping them find the right dataset to analyze. By providing a catalog of your data with consistent security enforcement, Lake Formation makes it easier for your analysts and data scientists to use their preferred analytics service. They can use Amazon EMR for Apache Spark (in beta), Amazon Redshift, or Amazon Athena on diverse datasets that are now housed in a single data lake. Users can also combine these services without having to move data between silos.

Question 33: What are the three stages to set up a data lake using AWS Lake Formation? (SELECT THREE)

A) Register the storage location

B) Create a database

C) Populate the database

D) Grant permissions

ANSWER33:
A B and D

Notes/Hint33:
Register the storage location
Lake Formation manages access to designated storage locations within Amazon S3. Register the storage locations that you want to be part of the data lake.

Create a database

Lake Formation organizes data into a catalog of logical databases and tables. Create one or more databases and then automatically generate tables during data ingestion for common workflows.

Grant permissions

Lake Formation manages access for IAM users, roles, and Active Directory users and groups via flexible database, table, and column permissions. Grant permissions to one or more resources for your selected users.

Question 34: Which of the following AWS Lake Formation tasks are performed by the AWS Glue service? (SELECT THREE)

A) ETL code creation and job monitoring

B) Blueprints to create workflows

C) Data catalog and serverless architecture

D) Simplify securty management

ANSWER34:
A B and C

Notes/Hint34:
Lake Formation leverages a shared infrastructure with AWS Glue, including console controls, ETL code creation and job monitoring, blueprints to create workflows for data ingest, the same data catalog, and a serverless architecture. While AWS Glue focuses on these types of functions, Lake Formation encompasses all AWS Glue features AND provides additional capabilities designed to help build, secure, and manage a data lake. See the AWS Glue features page for more de

Question 35: A digital media customer needs to quickly build a data lake solution for the data housed in a PostgreSQL database. As a solutions architect, what service and feature would meet this requirement?

A) Copy PostgreSQL data to an Amazon S3 bucket and build a data lake using AWS Lake Formation

B) Use AWS Lake Formation blueprints

C) Build a data lake manually

D) Build an analytics solution by directly accessing the database.

ANSWER35:
B

Notes/Hint35:
A blueprint is a data management template that enables you to easily ingest data into a data lake. Lake Formation provides several blueprints, each for a predefined source type, such as a relational database or AWS CloudTrail logs. From a blueprint, you can create a workflow. Workflows consist of AWS Glue crawlers, jobs, and triggers that are generated to orchestrate the loading and update of data. Blueprints take the data source, data target, and schedule as input to configure the workflow.

Question 36: AWS Lake Formation has a set of suggested personas and IAM permissions. Which is a required persona?

A) Data lake administrator

B) Data engineer

C) Data analyst

D) Business analyst

ANSWER36:
A

Notes/Hint36:
Data lake administrator (Required)
A user who can register Amazon S3 locations, access the Data Catalog, create databases, create and run workflows, grant Lake Formation permissions to other users, and view AWS CloudTrail logs. The user has fewer IAM permissions than the IAM administrator but enough to administer the data lake. Cannot add other data lake administrators.

Data engineer (Optional) A user who can create and run crawlers and workflows and grant Lake Formation permissions on the Data Catalog tables that the crawlers and workflows create.

Data analyst (Optional) A user who can run queries against the data lake using, for example, Amazon Athena. The user has only enough permissions to run queries.

Business analyst (Optional) Generally, an end-user application specific persona that would query data and resource using a workflow role.

Question 37: Which three types of blueprints does AWS Lake Formation support? (SELECT THREE)

A) ETL code creation and job monitoring

B) Database snapshot

C) Incremental database

D) Log file sources (AWS CloudTrail, ELB/ALB logs)

ANSWER37:
B C and D

Notes/Hint37:
AWS Lake Formation blueprints simplify and automate creating workflows. Lake Formation provides the following types of blueprints:
• Database snapshot – Loads or reloads data from all tables into the data lake from a JDBC source. You can exclude some data from the source based on an exclude pattern.

• Incremental database – Loads only new data into the data lake from a JDBC source, based on previously set bookmarks. You specify the individual tables in the JDBC source database to include. For each table, you choose the bookmark columns and bookmark sort order to keep track of data that has previously been loaded. The first time that you run an incremental database blueprint against a set of tables, the workflow loads all data from the tables and sets bookmarks for the next incremental database blueprint run. You can therefore use an incremental database blueprint instead of the database snapshot blueprint to load all data, provided that you specify each table in the data source as a paramete

• Log file – Bulk loads data from log file sources, including AWS CloudTrail, Elastic Load Balancing logs, and Application Load Balancer logs.

Question 38: Which one of the following is the best description of the capabilities of Amazon QuickSight?

A) Automated configuration service build on AWS Glue

B) Fast, serverless, business intelligence service

C) Fast, simple, cost-effective data warehousing

D) Simple, scalable, and serverless data integration

ANSWER38:
B C and D

Notes/Hint38:

B. Scalable, serverless business intelligence service is the correct choice.

See the brief descriptions of several AWS Analytics services below:

AWS Lake Formation Build a secure data lake in days using Glue blueprints and workflows

Amazon QuickSight Scalable, serverless, embeddable, ML-powered BI Service built for the cloud

Amazon Redshift Analyze all of your data with the fastest and most widely used cloud data warehouse

AWS Glue Simple, scalable, and serverless data integration

Question 39: Which benefits are provided by Amazon Redshift? (Select TWO)

A) Analyze Data stored in your data lake

B) Maintain performance at scale

C) Focus effort on Data warehouse administration

D) Store all the data to meet analytics need

E) Amazon Redshift includes enterprise-level security and compliance features.

ANSWER38:
A and B

Notes/Hint38:
A is correct – With Amazon Redshift, you can analyze all your data, including exabytes of data stored in your Amazon S3 data lake.
B is correct – Amazon Redshift provides consistent performance at scale.

• C is incorrect – Amazon Redshift is a fully managed data warehouse solution. It includes automations to reduce the administrative overhead traditionally associated with data warehouses. When using Amazon Redshift, you can focus your development effort on strategic data analytics solutions.

• D is incorrect – With Amazon Redshift features—such as Amazon Redshift Spectrum, materialized views, and federated query—you can analyze data where it is stored in your data lake or AWS databases. This capability provides flexibility to meet new analytics requirements without the cost, time, or complexity of moving large volumes of data between solutions.

• Answer E is incorrect – Amazon Redshift includes enterprise-level security and compliance features.

Djamga Data Sciences Big Data – Data Analytics Youtube Playlist

2- Prepare for Your AWS Certification Exam

3- LinuxAcademy

Big Data – Data Analytics Jobs:

Big Data – Data Analytics – Data Sciences Latest News:

Question: How is SQL used in real data science jobs?

DATA ANALYTICS Q&A:

Can I know what is big data?

(MySQL) When I save an invoice, the records save in more than 5 tables (ex: invoice, stock, etc). Save, if only, all the tables received all the data successfully otherwise reject that invoice which should not impact any table?

How can I become a data scientist?

Data analysis made easy: Text2Code for Jupyter notebook

What should be the learning sequence (among the following skills) to become a Data scientist – Tableau, Data Analysis, SQL, Data Science and Python (please indicate if any skill(s) is redundant?

What’s the best thing a data scientist can do for his career?

What is the best way to learn SQL for data science?

Learn Deep Learning With Python Tutorials Complete Guide…

How can I become a data scientist?

What are some struggles for people who are trying to break into the Data Science field?

Why should a data scientist work in a startup?

Why does a Data scientist needs to learn SQL when all the data exploration and data munging can be done in R or Python?

As data science changes over the next decade, what are the most crucial skills to learn to stay relevant?

Is Machine Learning a good choice for a career?

How do I learn more advanced SQL?

How can I become a data scientist?

Where can I intuitively learn SQL Joins specifically?

What is the most overrated thing about data science?

Is SQL an easy programming language?

Can a position as a data analyst lead to a position as a data scientist?

In layman’s terms (you naughty experts), can you explain how huge corporations like Google, Microsoft, Facebook, etc. upgrade their data centres and servers?

What are some of the natural careers people transition into after 5-7 years in data science/analytics?

Why would people, especially data scientists (maybe those in the industry) use SQL, rather than Pandas?

How much level of SQL knowledge is required for data analytics?

How do I learn data science (step-by-step)?

How do I get data science, computer vision, and machine learning tutorial apps?

Roughly speaking, how long should it take a person with no programming background (who already knows probability & statistics) to learn how to use R and Python for data analytics well enough to land a decent job as a data analyst?

How can I become a data scientist or a data analyst?

Does data science have good potential, or is it only hype?

What are some wrong ways to learn data science?

Is Data Science actually in demand? What skills can make me launch a fruitful career in Data Science?

Which are the best analytics and data visualization companies who implement Tableau?

Will the salary of data scientists decrease a lot in 2025? Is it worth starting a degree in it?

Will there be more data science and machine learning jobs in the future? I’m wondering if it is a good career choice.

Here’s a all list of the popular Data Science Tools used by Data Scientists !!!!…

How should you answer the question “How’s your SQL skills?” when asked by the data scientist hiring manager?

Are CS questions part of a data scientist interview at Facebook and are there interview questions position specific?

Can I work in data science if I don’t like programming?

How to organize data cleaning scripts

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Clever Questions, Answers, Resources about:

Data Sciences
Big Data
Data Analytics
Data Sciences
Databases
Data Streams
Large DataSets

What Is a Data Scientist?

Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician. – Josh Wills
Data scientists apply sophisticated quantitative and computer science skills to both structure and analyze massive stores or continuous streams of unstructured data, with the intent to derive insights and prescribe action. – Burtch Works Data Science Salary Survey, May 2018
More than anything, what data scientists do is make discoveries while swimming in data… In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data. – Data Scientist: The Sexiest Job of the 21st Century, Harvard Business Review

Do All Data Scientists Hold Graduate Degrees?

Data scientists are highly educated. With exceedingly rare exception, every data scientist holds at least an undergraduate degree. 91% of data scientists in 2018 held advanced degrees. The remaining 9% all held undergraduate degrees. Furthermore,

25% of data scientists hold a degree in statistics or mathematics,
20% have a computer science degree,
an additional 20% hold a degree in the natural sciences, and
18% hold an engineering degree.

The remaining 17% of surveyed data scientists held degrees in business, social science, or economics.

How Are Data Scientists Different From Data Analysts?

Broadly speaking, the roles differ in scope: data analysts build reports with narrow, well-defined KPIs. Data scientists often to work on broader business problems without clear solutions. Data scientists live on the edge of the known and unknown.

We’ll leave you with a concrete example: A data analyst cares about profit margins. A data scientist at the same company cares about market share.

How Is Data Science Used in Medicine?

Data science in healthcare best translates to biostatistics. It can be quite different from data science in other industries as it usually focuses on small samples with several confounding variables.

How Is Data Science Used in Manufacturing?

Data science in manufacturing is vast; it includes everything from supply chain optimization to the assembly line.

What are data scientists paid?

Most people are attracted to data science for the salary. It’s true that data scientists garner high salaries compares to their peers. There is data to support this: The May 2018 edition of the BurtchWorks Data Science Salary Survey, annual salary statistics were

Note the above numbers do not reflect total compensation which often includes standard benefits and may include company ownership at high levels.

How will data science evolve in the next 5 years?

Will AI replace data scientists?

What is the workday like for a data scientist?

It’s common for data scientists across the US to work 40 hours weekly. While company culture does dictate different levels of work life balance, it’s rare to see data scientists who work more than they want. That’s the virtue of being an expensive resource in a competitive job market.

How do I become a Data Scientist?

The roadmap given to aspiring data scientists can be boiled down to three steps:

Earning an undergraduate and/or advanced degree in computer science, statistics, or mathematics,
Building their portfolio of SQL, Python, and R skills, and
Getting related work experience through technical internships.

All three require a significant time and financial commitment.

There used to be a saying around datascience: The road into a data science starts with two years of university-level math.

What Should I Learn? What Order Do I Learn Them?

This answer assumes your academic background ends with a HS diploma in the US.

Python
Differential Calculus
Integral Calculus
Multivariable Calculus
Linear Algebra
Probability
Statistics

Some follow up questions and answers:

Why Python first?

Python is a general purpose language. R is used primarily by statisticians. In the likely scenario that you decide data science requires too much time, effort, and money, Python will be more valuable than your R skills. It’s preparing you to fail, sure, but in the same way a savings account is preparing you to fail.

When do I start working with data?

You’ll start working with data when you’ve learned enough Python to do so. Whether you’ll have the tools to have any fun is a much more open-ended question.

How long will this take me?

Assuming self-study and average intelligence, 3-5 years from start to finish.

How Do I Learn Python?

If you don’t know the first thing about programming, start with MIT’s course in the curated list.

These modules are the standard tools for data analysis in Python:

pandas (and by extension, numpy)Check out Minimally Sufficient Pandas for style guides and best practices.
matplotlib and seaborn See /u/rhiever’s response to How do you decide between the plotting libraries: Matplotlib, Seaborn, Bokeh?Don’t worry about bokeh or dash unless you have a personal interest in interactive visualizations.
scipy and scikit-learnInternalize the .fit() and .predict() pattern.

Curated Threads & Resources

MIT’s Introduction to Computer Science and Programming in Python A free, archived course taught at MIT in the fall 2016 semester.
Data Scientist with Python Career Track | DataCamp The first courses are free, but unlimited access costs $29/month. Users usually report a positive experience, and it’s one of the better hands-on ways to learn Python.
Sentdex’s (Harrison Kinsley) Youtube Channel Related to Python Programming Tutorials
/r/learnpython is an active sub and very useful for learning the basics.

How Do I Learn R?

If you don’t know the first thing about programming, start with R for Data Science in the curated list.

These modules are the standard tools for data analysis in Python:

Curated Threads & Resources

R for Data Science by Hadley WickhamA free ebook full of succinct code examples. Terrific for learning tidyverse syntax.Folks with some math background may prefer the free alternative, Introduction to Statistical Learning.
Data Scientist with R Career Track | DataCamp The first courses are free, but unlimited access costs $29/month. Users usually report a positive experience, and it’s one of the few hands-on ways to learn R.
R Inferno Learners with a CS background will appreciate this free handbook explaining how and why R behaves the way that it does.

How Do I Learn SQL?

Prioritize the basics of SQL. i.e. when to use functions like POW, SUM, RANK; the computational complexity of the different kinds of joins.

Concepts like relational algebra, when to use clustered/non-clustered indexes, etc. are useful, but (almost) never come up in interviews.

You absolutely do not need to understand administrative concepts like managing permissions.

Finally, there are numerous query engines and therefore numerous dialects of SQL. Use whichever dialect is supported in your chosen resource. There’s not much difference between them, so it’s easy to learn another dialect after you’ve learned one.

Curated Threads & Resources

The SQL Tutorial for Data Analysis | Mode.com
Introduction to Databases A Free MOOC supported by Stanford University.
SQL Queries for Mere MortalsA $30 book highly recommended by /u/karmanujan

How Do I Learn Calculus?

Fortunately (or unfortunately), calculus is the lament of many students, and so resources for it are plentiful. Khan Academy mimics lectures very well, and Paul’s Online Math Notes are a terrific reference full of practice problems and solutions.

Calculus, however, is not just calculus. For those unfamiliar with US terminology,

Calculus I is differential calculus.
Calculus II is integral calculus.
Calculus III is multivariable calculus.
Calculus IV is differential equations.

Differential and integral calculus are both necessary for probability and statistics, and should be completed first.

Multivariable calculus can be paired with linear algebra, but is also required.

Differential equations is where consensus falls apart. The short it is, they’re all but necessary for mathematical modeling, but not everyone does mathematical modeling. It’s another tool in the toolbox.

Curated Threads & Resources about Data Science and Data Analytics

Khan AcademyDifferential Calculus Integral Calculus Multivariable Calculus Differential Equations
Paul’s Online Math NotesDifferential Calculus Integral Calculus Multivariable Calculus

How Do I Learn Probability?

Probability is not friendly to beginners. Definitions are rooted in higher mathematics, notation varies from source to source, and solutions are frequently unintuitive. Probability may present the biggest barrier to entry in data science.

It’s best to pick a single primary source and a community for help. If you can spend the money, register for a university or community college course and attend in person.

The best free resource is MIT’s 18.05 Introduction to Probability and Statistics (Spring 2014). Leverage /r/learnmath, /r/learnmachinelearning, and /r/AskStatistics when you get inevitably stuck.

How Do I Learn Linear Algebra?

Curated Threads & Resources https://www.youtube.com/watch?v=fNk_zzaMoSs&index=1&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

What does the typical data science interview process look like?

For general advice, Mastering the DS Interview Loop is a terrific article. The community discussed the article here.

Briefly summarized, most companies follow a five stage process:

Coding Challenge: Most common at software companies and roles contributing to a digital product.
HR Screen
Technical Screen: Often in the form of a project. Less frequently, it takes the form of a whiteboarding session at the onsite.
Onsite: Usually the project from the technical screen is presented here, followed by a meeting with the director overseeing the team you’ll join.
Negotiation & Offer

Mastering the DS Interview Loop

Preparation:

Practice questions on Leetcode which has both SQL and traditional data structures/algorithm questions
Review Brilliant for math and statistics questions.
SQL Zoo and Mode Analytics both offer various SQL exercises you can solve in your browser.

Tips:

Before you start coding, read through all the questions. This allows your unconscious mind to start working on problems in the background.
Start with the hardest problem first, when you hit a snag, move to the simpler problem before returning to the harder one.
Focus on passing all the test cases first, then worry about improving complexity and readability.
If you’re done and have a few minutes left, go get a drink and try to clear your head. Read through your solutions one last time, then submit.
It’s okay to not finish a coding challenge. Sometimes companies will create unreasonably tedious coding challenges with one-week time limits that require 5–10 hours to complete. Unless you’re desperate, you can always walk away and spend your time preparing for the next interview.

Remember, interviewing is a skill that can be learned, just like anything else. Hopefully, this article has given you some insight on what to expect in a data science interview loop.

The process also isn’t perfect and there will be times that you fail to impress an interviewer because you don’t possess some obscure piece of knowledge. However, with repeated persistence and adequate preparation, you’ll be able to land a data science job in no time!

What does the Airbnb data science interview process look like? [Coming soon]

What does the Facebook data science interview process look like? [Coming soon]

What does the Uber data science interview process look like? [Coming soon]

What does the Microsoft data science interview process look like? [Coming soon]

What does the Google data science interview process look like? [Coming soon]

What does the Netflix data science interview process look like? [Coming soon]

What does the Apple data science interview process look like? [Coming soon]

Real life enterprise databases are orders of magnitude more complex than the “customers, products, orders” examples used as teaching tools. SQL as a language is actually, IMO, a relatively simple language (the db administration component can get complex, but mostly data scientists aren’t doing that anyways). SQL is an incredibly important skill though for any DS role. I think when people emphasize SQL, what they really are talking about is the ability to write queries that interrogate the data and discover the nuances behind how it is collected and/or manipulated by an application before it is written to the dB. For example, is the employee’s phone number their current phone number or does the database store a history of all previous phone numbers? Critically important questions for understanding the nature of your data, and it doesn’t necessarily deal with statistics! The level of syntax required to do this is not that sophisticated, you can get pretty damn far with knowledge of all the joins, group by/analytical functions, filtering and nesting queries. In many cases, the data is too large to just select * and dump into a csv to load into pandas, so you start with SQL against the source. In my mind it’s more important for “SQL skills” to know how to generate hypotheses (that will build up to answering your business question) that can be investigated via a query than it is to be a master of SQL’s syntax. Just my two cents though!