Big Data and Data Analytics 101 – Top 100 AWS Certified Data Analytics Specialty Certification Questions and Answers Dumps

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Top 100 AWS Certified Data Analytics Specialty Certification Questions and Answers Dumps

 

If you’re looking to take your data analytics career to the next level, then this AWS Data Analytics Specialty Certification Exam Preparation blog is a must-read! With over 100 exam questions and answers, plus data science and data analytics interview questions, cheat sheets and more, you’ll be fully prepared to ace the DAS-C01 exam. 

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In this blog, we talk about big data and data analytics; we also give you the last updated top 100 AWS Certified Data Analytics – Specialty Questions and Answers Dumps

Top 100 AWS Certified Data Analytics Specialty Certification Questions and Answers Dumps
AWS Data analytics DAS-C01 Exam Prep

The AWS Certified Data Analytics – Specialty (DAS-C01) examination is intended for individuals who perform in a data analytics-focused role. This exam validates an examinee’s comprehensive understanding of using AWS services to design, build, secure, and maintain analytics solutions that provide insight from data.

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The AWS Certified Data Analytics – Specialty (DAS-C01) covers the following domains:

Domain 1: Collection 18%

Domain 2: Storage and Data Management 22%

Domain 3: Processing 24%

Domain 4: Analysis and Visualization 18%

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Domain 5: Security 18%

data analytics specialty
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Below are the Top 100 AWS Certified Data Analytics – Specialty Questions and Answers Dumps and References

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Question1: What combination of services do you need for the following requirements: accelerate petabyte-scale data transfers, load streaming data, and the ability to create scalable, private connections. Select the correct answer order.

A) Snowball, Kinesis Firehose, Direct Connect

B) Data Migration Services, Kinesis Firehose, Direct Connect

C) Snowball, Data Migration Services, Direct Connect

D) Snowball, Direct Connection, Kinesis Firehose

ANSWER1:

A

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Notes/Hint1:

AWS has many options to help get data into the cloud, including secure devices like AWS Import/Export Snowball to accelerate petabyte-scale data transfers, Amazon Kinesis Firehose to load streaming data, and scalable private connections through AWS Direct Connect.

Reference1: Big Data Analytics Options 


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AWS Data analytics DAS-C01 Exam Prep

 


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ANSWER2:

C

Notes/Hint2:

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:

B

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

AWS Data analytics DAS-C01 Exam Prep

 

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:

C

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

 

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:

B

Notes/Hint5: 

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

Reference5: Amazon Kinesis

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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:

B

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:

C

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

AWS Data analytics DAS-C01 Exam Prep

 

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:

D

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:

A

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

 

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:

D

Notes/Hint10: 

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

Reference10: The IoT rules engine

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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:

B

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:

B

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:

C

Notes/Hint14: 

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

Reference14: Direct Connect

AWS Data analytics DAS-C01 Exam Prep

 

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:

D

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:

A

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:

B

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

AWS Data analytics DAS-C01 Exam Prep

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:

D

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:

C

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:

B

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
 
 

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
 

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:

DATA ANALYTICS Q&A:

 
 

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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:

  1. Earning an undergraduate and/or advanced degree in computer science, statistics, or mathematics,
  2. Building their portfolio of SQL, Python, and R skills, and
  3. 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.

  1. Python
  2. Differential Calculus
  3. Integral Calculus
  4. Multivariable Calculus
  5. Linear Algebra
  6. Probability
  7. 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:

Curated Threads & Resources

  1. MIT’s Introduction to Computer Science and Programming in Python A free, archived course taught at MIT in the fall 2016 semester.
  2. 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.
  3. Sentdex’s (Harrison Kinsley) Youtube Channel Related to Python Programming Tutorials
  4. /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

  1. 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.
  2. 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.
  3. 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

  1. The SQL Tutorial for Data Analysis | Mode.com
  2. Introduction to Databases A Free MOOC supported by Stanford University.
  3. 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

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:

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

Preparation:

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

Tips:

  1. Before you start coding, read through all the questions. This allows your unconscious mind to start working on problems in the background.
  2. Start with the hardest problem first, when you hit a snag, move to the simpler problem before returning to the harder one.
  3. Focus on passing all the test cases first, then worry about improving complexity and readability.
  4. 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.
  5. 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]

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

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!

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Data Visualization example: 12000 Years of Human Population Dynamic

[OC] 12,000 years of human population dynamics from dataisbeautiful

Human population density estimates based on the Hyde 3.2 model.

Capitol insurrection arrests per million people by state

[OC] Capitol insurrection arrests per million people by state from dataisbeautiful

Data Source: Made in Google Sheets using data from this USA Today article (for the number of arrests by arrestee’s home state) and this spreadsheet of the results of the 2020 Census (for the population of each state and DC in 2020, which was used as the denominator in calculating arrests/million people).

AWS Data analytics DAS-C01 Exam Prep

For more information about analytics architecture, visit the AWS Big Data Blog: AWS serverless data analytics pipeline reference architecture here

Basic Data Lake Architecture

Data Analytics Architecture on AWS

Data Analytics Architecture on AWS
Data Analytics Architecture on AWS

Data Analytics Process

Data Analytics Process
Data Analytics Process

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Data Lake Storage:

Data Lake STorage on AWS
Data Lake STorage on AWS – S3

 

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Event Driven Data Analytics Workflow on AWS

Event Driven Data Analytics Workflow on AWS
Event Driven Data Analytics Workflow on AWS

What is a Data Lake?

AWS Data lake

What is a Data Warehouse?

Data Warehouse
Data Warehouse

What are benefits of a data warehouse?

• Informed decision making

• Consolidated data from many sources

• Historical data analysis

• Data quality, consistency, and accuracy

• Separation of analytics processing from transactional databases

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Data Lake vs Data Warehouse – Comparison

Data Lake vs Data Warehouse comparison

A data warehouse is specially designed for data analytics, which identifies relationships and trends across large amounts of data. A database is used to capture and store data, such as the details of a transaction. Unlike a data warehouse, a data lake is a centralized repository for structured, semi-structured, and unstructured data. A data warehouse organizes data in a tabular format (or schema) that enables SQL queries on the data. But not all applications require data to be in tabular format. Some applications can access data in the data lake even if it is “semi-structured” or unstructured. These include big data analytics, full-text search, and machine learning.

An AWS data lake only has a storage charge for the data. No servers are necessary for the data to be stored and accessed. In the case of Amazon Athena, also, there are no additional charges for processing. Data warehouse enable fast queries of structured data from transactional systems for batch reports, business intelligence, and visualization use cases. A data lake stores data without regard to its structure. Data scientists, data analysts, and business analysts use the data lake. They support use cases such as machine learning, predictive analytics, and data discovery and profiling.

Transactional Data Ingestion

Transactional Data Ingestion on AWS
Transactional Data Ingestion on AWS

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Streaming Data Ingestion on AWS
Streaming Data Ingestion on AWS

Structured Query Language (SQL)

SQL Structured Query Language
SQL Structured Query Language

Data definition language (DDL) refers to the subset of SQL commands that define data structures and objects such as databases, tables, and views. DDL commands include the following:

• CREATE: used to create a new object.

• DROP: used to delete an object.

• ALTER: used to modify an object.

• RENAME: used to rename an object.

• TRUNCATE: used to remove all rows from a table without deleting the table itself.

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Data manipulation language (DML) refers to the subset of SQL commands that are used to work with data. DML commands include the following:

• SELECT: used to request records from one or more tables.

• INSERT: used to insert one or more records into a table.

• UPDATE: used to modify the data of one or more records in a table.

• DELETE: used to delete one or more records from a table.

• EXPLAIN: used to analyze and display the expected execution plan of a SQL statement.

• LOCK: used to lock a table from write operations (INSERT, UPDATE, DELETE) and prevent concurrent operations from conflicting with one another.

Data control language (DCL) refers to the subset of SQL commands that are used to configure permissions to objects. DCL commands include:

• GRANT: used to grant access and permissions to a database or object in a database, such as a schema or table.

• REVOKE: used to remove access and permissions from a database or objects in a database.

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Comparison of OLTP and OLAP

OLTP vs OLAP
OLTP vs OLAP

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What is Amazon Macie?

Amazon Macie
Amazon Macie

Businesses are responsible to identify and limit disclosure of sensitive data such as personally identifiable information (PII) or proprietary information. Identifying and masking sensitive information is time consuming, and becomes more complex in data lakes with various data sources and formats and broad user access to published data sets.

Amazon Macie is a fully managed data security and privacy service that uses machine learning and pattern matching to discover sensitive data in AWS. Macie includes a set of managed data identifiers which automatically detect common types of sensitive data. Examples of managed data identifiers include keywords, credentials, financial information, health information, and PII. You can also configure custom data identifiers using keywords or regular expressions to highlight organizational proprietary data, intellectual property, and other specific scenarios. You can develop security controls that operate at scale to monitor and remediate risk automatically when Macie detects sensitive data. You can use AWS Lambda functions to automatically turn on encryption for an Amazon S3 bucket where Macie detects sensitive data. Or automatically tag datasets containing sensitive data, for inclusion in orchestrated data transformations or audit reports.

Amazon Macie can be integrated into the data ingestion and processing steps of your data pipeline. This approach avoids inadvertent disclosures in published data sets by detecting and addressing the sensitive data as it is ingested and processed. Building the automated detection and processing of sensitive data into your ETL pipelines simplifies and standardizes handling of sensitive data at scale.

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What is AWS Glue DataBrew?

AWS Glue DataBrew
AWS Glue DataBrew

AWS Glue DataBrew is a visual data preparation tool that simplifies cleaning and normalizing datasets in preparation for use in analytics and machine learning.

• Profile data quality, identifying patterns and automatically detecting anomalies.

• Clean and normalize data using over 250 pre-built transformations, without writing code.

• Visually map the lineage of your data to understand data sources and transformation history.

• Save data cleaning and normalization workflows for automatic application to new data.

Data processed in AWS Glue DataBrew is immediately available for use in analytics and machine learning projects.

Learn more about the built-in transformations available in AWS Glue DataBrew in the Recipe actions reference: https://docs.aws.amazon.com/databrew/latest/dg/recipe-actions-reference.html

AWS Data Analytics Specialty Certification DAS-C01 Exam Prep on iOS

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What is AWS Glue?

AWS Glue
AWS Glue

AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores and data streams. AWS Glue consists of a central metadata repository known as the AWS Glue Data Catalog, an ETL engine that automatically generates Python or Scala code, and a flexible scheduler that handles dependency resolution, job monitoring, and retries. AWS Glue can run your ETL jobs as new data arrives. For example, you can use an AWS Lambda function to trigger your ETL jobs to run as soon as new data becomes available in Amazon S3. You can also register this new dataset in the

AWS Glue Data Catalog as part of your ETL jobs.

AWS Glue is serverless, so there’s no infrastructure to set up or manage.

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AWS Glue Data Catalog The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos, and use that metadata to query and transform the data. Once the data is cataloged, it is immediately available for search and query using Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.

You can use AWS Identity and Access Management (IAM) policies to control access to the data sources managed by the AWS Glue Data Catalog. The Data Catalog also provides comprehensive audit and governance capabilities, with schema-change tracking and data access controls.

AWS Glue crawler

AWS Glue crawlers can scan data in all kinds of repositories, classify it, extract schema information from it, and store the metadata automatically in the AWS Glue Data Catalog.

AWS Glue ETL

AWS Glue can run your ETL jobs as new data arrives. For example, you can use an AWS Lambda function to trigger your ETL jobs to run as soon as new data becomes available in Amazon S3. You can also register this new dataset in the AWS Glue Data Catalog as part of your ETL jobs.

AWS Glue Studio

AWS Glue Studio provides a graphical interface to create, run, and monitor extract, transform, and load (ETL) jobs in AWS Glue. You can visually compose data transformation workflows and seamlessly run them on AWS Glue’s Apache Spark-based serverless ETL engine. AWS Glue Studio also offers tools to monitor ETL workflows and validate that they are operating as intended.

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What is Amazon Athena?

Amazon Athena
Amazon Athena: Serverless Query Engine

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to set up or manage, and you can start analyzing data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3. To get started, just log into the Amazon Athena console, define your schema, and start querying. Athena uses Presto with full standard SQL support. It works with a variety of standard data formats, including CSV, JSON, ORC, Apache Parquet and Avro. While Athena is ideal for quick, ad-hoc querying, it can also handle complex analysis, including large joins, window functions, and arrays.

Amazon Athena helps you analyze data stored in Amazon S3. You can use Athena to run ad-hoc queries using ANSI SQL, without the need to aggregate or load the data into Athena. It can process unstructured, semi-structured, and structured datasets. Examples include CSV, JSON, Avro or columnar data formats such as Apache Parquet and Apache ORC. Athena integrates with Amazon QuickSight for easy visualization. You can also use Athena to generate reports or to explore data with business intelligence tools or SQL clients, connected via an ODBC or JDBC driver.

The tables and databases that you work with in Athena to run queries are based on metadata. Metadata is data about the underlying data in your dataset. How that metadata describes your dataset is called the schema. For example, a table name, the column names in the table, and the data type of each column are schema, saved as metadata, that describe an underlying dataset. In Athena, we call a system for organizing metadata a data catalog or a metastore. The combination of a dataset and the data catalog that describes it is called a data source.

The relationship of metadata to an underlying dataset depends on the type of data source that you work with. Relational data sources like MySQL, PostgreSQL, and SQL Server tightly integrate the metadata with the dataset. In these systems, the metadata is most often written when the data is written. Other data sources, like those built using Hive, allow you to define metadata on-the-fly when you read the dataset. The dataset can be in a variety of formats; for example, CSV, JSON, Parquet, or Avro.

AWS Data Analytics Specialty Certification DAS-C01 Exam Prep on iOS

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What is AWS Lake Formation?

What is AWS Lake Formation?
What is AWS Lake Formation?

Lake Formation is a fully managed service that enables data engineers, security officers, and data analysts to build, secure, manage, and use your data lake

To build your data lake in AWS Lake Formation, you must register an Amazon S3 location as a data lake. The Lake Formation service must have permission to write to the AWS Glue Data Catalog and to Amazon S3 locations in the data lake.

Next, identify the data sources to be ingested. AWS Lake formation can move data into your data lake from existing Amazon S3 data stores. Lake Formation can collect and organize datasets, such as logs from AWS CloudTrail, AWS CloudFront, detailed billing reports, or Elastic Load Balancing. You can ingest bulk or incremental datasets from relational, NoSQL, or non-relational databases. Lake Formation can ingest data from databases running in Amazon RDS or hosted in Amazon EC2. You can also ingest data from on-premises databases using Java Database Connectivity JDBC connectors. You can use custom AWS Glue jobs to load data from other databases or to ingest streaming data using Amazon Kinesis or Amazon DynamoDB.

AWS Lake Formation manages AWS Glue crawlers, AWS Glue ETL jobs, the AWS Glue Data Catalog, security settings, and access control:

• Lake Formation is an automated build environment based on AWS Glue.

• Lake Formation coordinates AWS Glue crawlers to identify datasets within the specified data stores and collect metadata for each dataset

• Lake Formation can perform transformations on your data, such as rewriting and organizing data into a consistent, analytics-friendly format. Lake Formation creates transformation templates and schedules AWS Glue jobs to prepare and optimize your data for analytics. Lake Formation also helps clean your data using FindMatches, an ML-based deduplication transform. AWS Glue jobs encapsulate scripts, such as ETL scripts, which connect to source data, process it, and write it out to a data target. AWS Glue triggers can start jobs based on a schedule or event, or on demand. AWS Glue workflows orchestrate AWS ETL jobs, crawlers, and triggers. You can define a workflow manually or use a blueprint based on commonly ingested data source types.

• The AWS Glue Data Catalog within the data lake persistently stores the metadata from raw and processed datasets. Metadata about data sources and targets is in the form of databases and tables. Tables store information about the underlying data, including schema information, partition information, and data location. Databases are collections of tables. Each AWS account has one data catalog per AWS Region.

• Lake Formation provides centralized access controls for your data lake, including security policy-based rules for users and applications by role. You can authenticate the users and roles using AWS IAM. Once the rules are defined, Lake Formation enforces them with table-and column-level granularity for users of Amazon Redshift Spectrum and Amazon Athena. Rules are enforced at the table-level in AWS Glue, which is normally accessed for administrators.

• Lake Formation leverages the encryption capabilities of Amazon S3 for data in the data lake. This approach provides automatic server-side encryption with keys managed by the AWS Key Management Service (KMS). S3 encrypts data in transit when replicating across Regions. You can separate accounts for source and destination Regions to further protect your data

AWS Data Analytics Specialty Certification DAS-C01 Exam Prep on iOS

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What is Amazon Quicksight?

Amazon QuickSight is a cloud-scale business intelligence (BI) service. In a single data dashboard, QuickSight gives decision-makers the opportunity to explore and interpret information in an interactive visual environment. QuickSight can include AWS data, third-party data, big data, spreadsheet data, SaaS data, B2B data, and more. QuickSight delivers fast and responsive query performance by using a robust in-memory engine (SPICE).

Scale from tens to tens of thousands of users

Amazon QuickSight has a serverless architecture that automatically scales to tens of thousands of users without the need to setup, configure, or manage your own servers.

Embed BI dashboards in your applications

With QuickSight, you can quickly embed interactive dashboards into your applications, websites, and portals.

Access deeper insights with Machine Learning

QuickSight leverages the proven machine learning (ML) capabilities of AWS. BI teams can perform advanced analytics without prior data science experience.

Ask questions of your data, receive answers

With QuickSight, you can quickly get answers to business questions asked in natural language with QuickSight’s new ML-powered natural language query capability, Q.

AWS Data Analytics Specialty Certification DAS-C01 Exam Prep on iOS

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What is SPICE?

SPICE
SPICE is the Super-fast, Parallel, In-memory Calculation Engine in QuickSight.

SPICE is the Super-fast, Parallel, In-memory Calculation Engine in QuickSight. SPICE is engineered to rapidly perform advanced calculations and serve data. The storage and processing capacity available in SPICE speeds up the analytical queries that you run against your imported data. By using SPICE, you save time because you don’t need to retrieve the data every time that you change an analysis or update a visual.

When you import data into a dataset rather than using a direct SQL query, it becomes SPICE data because of how it’s stored. SPICE is the Amazon QuickSight Super-fast, Parallel, In-memory Calculation Engine. It’s engineered to rapidly perform advanced calculations and serve data. In Enterprise edition, data stored in SPICE is encrypted at rest.

When you create or edit a dataset, you choose to use either SPICE or a direct query, unless the dataset contains uploaded files. Importing (also called ingesting) your data into SPICE can save time and money:

• Your analytical queries process faster.

• You don’t need to wait for a direct query to process.

• Data stored in SPICE can be reused multiple times without incurring additional costs. If you use a data source that charges per query, you’re charged for querying the data when you first create the dataset and later when you refresh the dataset.

AWS Data Analytics Specialty Certification DAS-C01 Exam Prep on iOS

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Serverless data lake reference architecture:

Serverless Data lake Reference Architecture
Serverless Data lake Reference Architecture

You can use AWS services as building blocks to build serverless data lakes and analytics pipelines. You can apply best practices on how to ingest, store, transform, and analyze structured and unstructured data at scale. Achieve the scale without needing to manage any storage or compute infrastructure. A decoupled, component-driven architecture allows you to start small and scale out slowly. You can quickly add new purpose-built components to one of six architecture layers to address new requirements and data sources.

This data lake-centric architecture can support business intelligence (BI) dashboarding, interactive SQL queries, big data processing, predictive analytics, and machine learning use cases.

• The ingestion layer includes protocols to support ingestion of structured, unstructured, or streaming data from a variety of sources.

• The storage layer provides durable, scalable, secure, and cost-effective storage of datasets across ingestion and processing.

• The landing zone stores data as ingested.

• Data engineers run initial quality checks to validate and cleanse data in the landing zone, producing the raw dataset.

• The processing layer creates curated datasets by further cleansing, normalizing, standardizing, and enriching data from the raw zone. The curated dataset is typically stored in formats that support performant and cost-effective access by the consumption layer.

• The catalog layer stores business and technical metadata about the datasets hosted in the storage layer.

• The consumption layer contains functionality for Search, Analytics, and Visualization. It integrates with the data lake storage, cataloging, and security layers. This integration supports analysis methods such as SQL, batch analytics, BI dashboards, reporting, and ML.

• The security and monitoring layer protects data within the storage layer and other resources in the data lake. This layer includes access control, encryption, network protection, usage monitoring, and auditing.

You can learn more about this reference architecture at AWS Big Data Blog: AWS serverless data analytics pipeline reference architecture: https://aws.amazon.com/blogs/big-data/aws-serverless-data-analytics-pipeline-reference-architecture/

What are Data Lakes Best Practices?

What are Data Lakes Best Practices?
What are Data Lakes Best Practices?

The main challenge with a data lake architecture is that raw data is stored with no oversight of the contents. To make the data usable, you must have defined mechanisms to catalog and secure the data. Without these mechanisms, data cannot be found or trusted, resulting in a “data swamp.” Meeting the needs of diverse stakeholders requires data lakes to have governance, semantic consistency, and access controls.

The Analytics Lens for the AWS Well-Architected Framework covers common analytics applications scenarios, including data lakes. It identifies key elements to help you architect your data lake according to best practices, including the following configuration notes:

• Decide on a location for data lake ingestion (that is, an S3 bucket). Select a frequency and isolation mechanism that meets your business needs.

• For Tier 2 Data, partition the data with keys that align to common query filter

. This enables pruning by common analytics tools that work on raw data files and increases performance

• Choose optimal file sizes to reduce Amazon S3 round trips during compute environment ingestion. Recommended: 512 MB – 1 GB in a columnar format (ORC/Parquet) per partition.

• Perform frequent scheduled compactions that align to the optimal file sizes noted previously. For example, compact into daily partitions if hourly files are too small.

• For data with frequent updates or deletes (that is, mutable data), either: o Temporarily store replicated data to a database like Amazon Redshift, Apache Hive, or Amazon RDS. Once the data becomes static, and then offload it to Amazon S3. Or, o Append the data to delta files per partition and compact it on a scheduled basis. You can use AWS Glue or Apache Spark on Amazon EMR for this processing.

With Tier 2 and Tier 3 Data being stored in Amazon S3, partition data using a high cardinality key. This is honored by Presto, Apache Hive, and Apache Spark and improves the query filter performance on that key

• Sort data in each partition with a secondary key that aligns to common filter queries. This allows query engines to skip files and get to requested data faster. For more information on the Analytics Lens for the AWS Well-Architected Framework, visit https://docs.aws.amazon.com/wellarchitected/latest/analytics-lens/data-lake.html

References:

For additional information on AWS data lakes and data analytics architectures, visit:

• AWS Well-Architected: Learn, measure, and build using architectural best practices: https://aws.amazon.com/architecture/well-architected

• AWS Lake Formation: Build a secure data lake in days: https://aws.amazon.com/lake-formation

• Getting Started with Amazon S3: https://aws.amazon.com/s3/getting-started

• Security in AWS Lake Formation: https://docs.aws.amazon.com/lake-formation/latest/dg/security.html 

AWS Lake Formation: How It Works: https://docs.aws.amazon.com/lake-formation/latest/dg/how-it-works.html

• AWS Lake Formation Dashboard: https://us-west-2.console.aws.amazon.com/lakeformation

• Data Lake Storage on AWS: https://aws.amazon.com/products/storage/data-lake-storage/

• Building Big Data Storage Solutions (Data Lakes) for Maximum Flexibility: https://docs.aws.amazon.com/whitepapers/latest/building-data-lakes/building-data-lake-aws.html

• Data Ingestion Methods: https://docs.aws.amazon.com/whitepapers/latest/building-data-lakes/data-ingestion-methods.html

Top 20 AWS Certified Associate SysOps Administrator Practice Quiz – Questions and Answers Dumps

AWS Certified Security – Specialty Questions and Answers Dumps

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Top 20 AWS Certified Associate SysOps Administrator Practice Quiz – Questions and Answers Dumps

What is the AWS Certified SysOps Administrator – Associate?

The AWS Certified SysOps Administrator – Associate (SOA-C01) examination is intended for individuals who have technical expertise in deployment, management, and operations on AWS.

The AWS Certified SysOps Administrator – Associate exam covers the following domains:

Domain 1: Monitoring and Reporting 22%

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Domain 2: High Availability 8%

Domain 3: Deployment and Provisioning 14%

Domain 4: Storage and Data Management 12%

Domain 5: Security and Compliance 18%


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Domain 6: Networking 14%

Domain 7: Automation and Optimization 12%

AWS Certified SysOps Administrator
AWS Certified SysOps Administrator

Top 200 Top 20 AWS Certified Associate SysOps Administrator  Practice Quiz Questions and Answers and References – SOA-C01:

Download Full PDF here

AWS Certified SysOps Administrator – Associate Study guide and Practice Exam
AWS Certified SysOps Administrator – Associate Study guide and Practice Exam

Question 1: Under which security model does AWS provide secure infrastructure and services, while the customer is responsible for secure operating systems, platforms, and data?

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ANSWER1:

C

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NOTES/HINT1: The Shared Responsibility Model is the security model under which AWS provides secure infrastructure and services, while the customer is responsible for secure operating systems, platforms, and data.

Question 2: Which type of testing method is used to compare a control system to a test system, with the goal of assessing whether changes applied to the test system improve a particular metric compared to the control system?

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ANSWER2:

A


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NOTES/HINT2: The side-by-side testing method is used to compare a control system to a test system, with the goal of assessing whether changes applied to the test system improve a particular metric compared to the control system.

Reference2: AWS Side by side testing 

Question 3: When BGP is used with a hardware VPN, the IPSec and the BGP connections must both be which of the following on the same user gateway device?


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ANSWER3:

B

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NOTES/HINT3: The IPSec and the BGP connections must both be terminated on the same user gateway device.

Reference3: IpSec and BGP in AWS

Question 4: Which pillar of the AWS Well-Architected Framework includes the ability to protect information, systems, and assets while delivering business value through risk assessments and mitigation strategies?

ANSWER4:

D

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NOTES/HINT4: Security is the pillar of the AWS Well-Architected Framework that includes the ability to protect information, systems, and assets while delivering business value through risk assessments and mitigation strategies.

Reference4: AWS Well-Architected Framework: Security

Question 5: Within the realm of Amazon S3 backups, snapshots are which of the following?

ANSWER5:

A

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NOTES/HINT: Within the realm of Amazon S3 backups, snapshots are block-based.

Reference5: Snapshots are block based

Question 6: Amazon VPC provides the option of creating a hardware VPN connection between remote customer networks and their Amazon VPC over the Internet using which encryption technology?

ANSWER6:

E

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NOTES/HINT6: Amazon VPC provides the option of creating a hardware VPN connection between remote customer networks and their Amazon VPC over the Internet using IPsec encryption technology.

Reference6: Amazon VPC IPSec Encryption

Question 7: To make a clean backup of a database, that database should be put into what mode before making a snapshot of it?

ANSWER7:

C

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NOTES/HINT7: To make a clean backup of a database, that database should be put into hot backup mode before making a snapshot of it.

Reference: AWS Prescriptive Backup Recovery Guide

Question 8: Which pillar of the AWS Well-Architected Framework includes the ability to use computing resources efficiently to meet system requirements, and to maintain that efficiency as demand changes and technologies evolve?

ANSWER8:

B

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NOTES/HINT8: Performance efficiency is the pillar of the AWS Well-Architected Framework that includes the ability to use computing resources efficiently to meet system requirements, and to maintain that efficiency as demand changes and technologies evolve.

Reference8: Performance Efficiency Pillar – AWS Well-Architected Framework

Question 9: AWS Storage Gateway supports which three configurations?

ANSWER9:

C

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NOTES/HINT9: AWS Storage Gateway supports Gateway-stored volumes, Gateway-cached volumes, and Gateway-virtual tape library.

Reference9: AWS Storage Gateway configurations

Question 10: With which of the following can you establish private connectivity between AWS and a data center, office, or co-location environment?

ANSWER10:

B

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NOTES/HINT10: With AWS Direct Connect you can establish private connectivity between AWS and a data center, office, or co-location environment.

Reference: AWS Direct Connect

Question 11: A company is migrating a legacy web application from a single server to multiple Amazon EC2 instances behind an Application Load Balancer (ALB). After the migration, users report that they are frequently losing their sessions and are being prompted to log in again. Which action should be taken to resolve the issue reported by users?

A) Purchase Reserved Instances.
B) Submit a request for a Spot block.
C) Submit a request for all Spot Instances.
D) Use a mixture of On-Demand and Spot Instances

ANSWER11:

D

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NOTES/HINT11: Legacy applications designed to run on a single server frequently store session data locally. When these applications are deployed on multiple instances behind a load balancer, user requests are routed to instances using the round robin routing algorithm. Session data stored on one instance would not be present on the others. By enabling sticky sessions, cookies are used to track user requests and keep subsequent requests going to the same instance.

Reference 11: Sticky Sessions

Question 12: An ecommerce company wants to lower costs on its nightly jobs that aggregate the current day’s sales and store the results in Amazon S3. The jobs run on multiple On-Demand Instances, and the jobs take just under 2 hours to complete. The jobs can run at any time during the night. If the job fails for any reason, it needs to be started from the beginning. Which solution is the MOST cost-effective based on these requirements?

A) Purchase Reserved Instances.

B) Submit a request for a Spot block.

C) Submit a request for all Spot Instances.

D) Use a mixture of On-Demand and Spot Instances.

ANSWER12:

B

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NOTES/HINT12: The solution will take advantage of Spot pricing, but by using a Spot block instead of Spot Instances, the company can be assured the job will not be interrupted.

Reference12: Spot Block

Question 13: A sysops team checks their AWS Personal Health Dashboard every week for upcoming AWS hardware maintenance events. Recently, a team member was on vacation and the team missed an event, which resulted in an outage. The team wants a simple method to ensure that everyone is aware of upcoming events without depending on an individual team member checking the dashboard. What should be done to address this?

A) Build a web scraper to monitor the Personal Health Dashboard. When new health events are detected, send a notification to an Amazon SNS topic monitored by the entire team.

B) Create an Amazon CloudWatch Events event based off the AWS Health service and send a notification to an Amazon SNS topic monitored by the entire team.

C) Create an Amazon CloudWatch Events event that sends a notification to an Amazon SNS topic monitored by the entire team to remind the team to view the maintenance events on the Personal Health Dashboard.

D) Create an AWS Lambda function that continuously pings all EC2 instances to confirm their health. Alert the team if this check fails.

ANSWER13:

B

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NOTES/HINT13: The AWS Health service publishes Amazon CloudWatch Events. CloudWatch Events can trigger Amazon SNS notifications. This method requires neither additional coding nor infrastructure. It automatically notifies the team of upcoming events, and does not depend upon brittle solutions like web scraping.

Reference 13: Amazon CloudWatch Events

Question14: An application running in a VPC needs to access instances owned by a different account and running in a VPC in a different AWS Region. For compliance purposes, the traffic must not traverse the public internet.
How should a sysops administrator configure network routing to meet these requirements?

A) Within each account, create a custom routing table containing routes that point to the other account’s virtual private gateway.

B) Within each account, set up a NAT gateway in a public subnet in its respective VPC. Then, using the public IP address from the NAT gateway, enable routing between the two VPCs.

C) From one account, configure a Site-to-Site VPN connection between the VPCs. Within each account, add routes in the VPC route tables that point to the CIDR block of the remote VPC.

D) From one account, create a VPC peering request. After an administrator from the other account accepts the request, add routes in the route tables for each VPC that point to the CIDR block of the peered VPC.

ANSWER14:

D

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NOTES/HINT14: A VPC peering connection enables routing using each VPC’s private IP addresses as if they were in the same network. Traffic using inter-Region VPC peering always stays on the global AWS backbone and never traverses the public internet.

Reference14: VPC Peering

Question15: An application running on Amazon EC2 instances needs to access data stored in an Amazon DynamoDB table.

Which solution will grant the application access to the table in the MOST secure manner?

A) Create an IAM group for the application and attach a permissions policy with the necessary privileges. Add the EC2 instances to the IAM group.

B) Create an IAM resource policy for the DynamoDB table that grants the necessary permissions to Amazon EC2.

C) Create an IAM role with the necessary privileges to access the DynamoDB table. Associate the role with the EC2 instances.

D) Create an IAM user for the application and attach a permissions policy with the necessary privileges. Generate an access key and embed the key in the application code.

ANSWER15:

C

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NOTES/HINT15: An IAM role can be used to provide permissions for applications that are running on Amazon EC2 instances
to make AWS API requests using temporary credentials.

Reference15: IAM Role

Question16: A third-party service uploads objects to Amazon S3 every night. Occasionally, the service uploads an incorrectly formatted version of an object. In these cases, the sysops administrator needs to recover an older version of the object.
What is the MOST efficient way to recover the object without having to retrieve it from the remote service?

A) Configure an Amazon CloudWatch Events scheduled event that triggers an AWS Lambda function that backs up the S3 bucket prior to the nightly job. When bad objects are discovered, restore the backed up version.

B) Create an S3 event on object creation that copies the object to an Amazon Elasticsearch Service (Amazon ES) cluster. When bad objects are discovered, retrieve the previous version from Amazon ES.

C) Create an AWS Lambda function that copies the object to an S3 bucket owned by a different account. Trigger the function when new objects are created in Amazon S3. When bad objects are discovered, retrieve the previous version from the other account.

D) Enable versioning on the S3 bucket. When bad objects are discovered, access previous versions with the AWS CLI or AWS Management Console.

ANSWER16:

D

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NOTES/HINT16: Enabling versioning is a simple solution; (A) involves writing custom code, (C) has no versioning, so the replication will overwrite the old version with the bad version if the error is not discovered quickly, and (B) will involve expensive storage that is not well suited for objects.

Reference16: Versioning

Question17: According to the AWS shared responsibility model, for which of the following Amazon EC2 activities is AWS responsible? (Select TWO.)
A) Configuring network ACLs
B) Maintaining network infrastructure
C) Monitoring memory utilization
D) Patching the guest operating system
E) Patching the hypervisor

ANSWER17:

D and E

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NOTES/HINT17: AWS provides security of the cloud, including maintenance of the hardware and hypervisor software supporting Amazon EC2. Customers are responsible for any maintenance or monitoring within an EC2 instance, and for configuring their VPC infrastructure.

Reference17: Security of the cloud

Question18: A security and compliance team requires that all Amazon EC2 workloads use approved Amazon Machine Images (AMIs). A sysops administrator must implement a process to find EC2 instances launched from unapproved AMIs.

Which solution will meet these requirements?
A) Create a custom report using AWS Systems Manager inventory to identify unapproved AMIs.
B) Run Amazon Inspector on each EC2 instance and flag the instance if it is using unapproved AMIs.
C) Use an AWS Config rule to identify unapproved AMIs.
D) Use AWS Trusted Advisor to identify the EC2 workloads using unapproved AMIs.

ANSWER18:

C

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NOTES/HINT18: AWS Config has a managed rule that handles this scenario.

Reference18: Managed Rule

Question19: A sysops administrator observes a large number of rogue HTTP requests on an Application Load Balancer. The requests originate from various IP addresses. These requests cause increased server load and costs.

What should the administrator do to block this traffic?
A) Install Amazon Inspector on Amazon EC2 instances to block the traffic.
B) Use Amazon GuardDuty to protect the web servers from bots and scrapers.
C) Use AWS Lambda to analyze the web server logs, detect bot traffic, and block the IP addresses in the security groups.
D) Use an AWS WAF rate-based rule to block the traffic when it exceeds a threshold.

ANSWER19:

D

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NOTES/HINT19: AWS WAF has rules that can protect web applications from HTTP flood attacks.

Reference19: HTTP Flood

Question20: A sysops administrator is implementing security group policies for a web application running on AWS.

An Elastic Load Balancer connects to a fleet of Amazon EC2 instances that connect to an Amazon RDS database over port 1521. The security groups are named elbSG, ec2SG, and rdsSG, respectively.
How should these security groups be implemented?
A) elbSG: allow port 80 and 443 from 0.0.0.0/0;
ec2SG: allow port 443 from elbSG;
rdsSG: allow port 1521 from ec2SG.

B) elbSG: allow port 80 and 443 from 0.0.0.0/0;
ec2SG: allow port 80 and 443 from elbSG and rdsSG;
rdsSG: allow port 1521 from ec2SG.

C) elbSG: allow port 80 and 443 from ec2SG;
ec2SG: allow port 80 and 443 from elbSG and rdsSG;
rdsSG: allow port 1521 from ec2SG.

D) elbSG: allow port 80 and 443 from ec2SG;
ec2SG: allow port 443 from elbSG;
rdsSG: allow port 1521 from elbSG.

ANSWER20: 

A

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NOTES/HINT20: elbSG must allow all web traffic (HTTP and HTTPS) from the internet. ec2SG must allow traffic from the load balancer only, in this case identified as traffic from elbSG. The database must allow traffic from the EC2 instances only, in this case identified as traffic from ec2SG.

Reference20: Allow all traffic

Question21: You are currently hosting multiple applications in a VPC and have logged numerous port scans coming in from a specific IP address block. Your security team has requested that all access from the offending IP address block be denied tor the next 24 hours. Which of the following is the best method to quickly and temporarily deny access from
the specified IP address block.

A) Create an AD policy to modify Windows Firewall settings on all hosts in the VPC to deny access from the IP address block
B) Modify the Network ACLs associated with all public subnets in the VPC to deny access from the IP address block
C) Add a rule to all of the VPC 5 Security Groups to deny access from the IP address block
D) Modify the Windows Firewall settings on all Amazon Machine Images (AMIs) that your organization uses in that VPC to deny access from the IP address block

ANSWER21:


C

NOTES22: Add a rule to all of the VPC 5 Security Groups to deny access from the IP address bloc

Reference22: VPC

Question 22: When preparing for a compliance assessment of your system built inside of AWS. what are three best-practices for you to prepare for an audit? Choose 3 answers

A) Gather evidence of your IT operational controls
B) Request and obtain applicable third-party audited AWS compliance reports and certifications
C) Request and obtain a compliance and security tour of an AWS data center for a pre-assessment security review
D) Request and obtain approval from AWS to perform relevant network scans and in-depth penetration tests of your system’s Instances and endpoint
E) Schedule meetings with AWS’s third-party auditors to provide evidence of AWS compliance that maps to your control objectives

ANSWER22:


B, D, E

NOTES22: AWS Security

Reference22: AWS Audit Manager

Question23: You have started a new job and are reviewing your company’s infrastructure on AWS You notice one web application where they have an Elastic Load Balancer (&B) in front of web instances in an Auto Scaling Group When you check the metrics for the ELB in CloudWatch you see four healthy instances In Availability Zone (AZ) A and zero in AZ B There are zero unhealthy instances.
What do you need to fix to balance the instances across AZs?

A) Set the ELB to only be attached to another AZ
B) Make sure Auto Scaling is configured to launch in both AZs
C) Make sure your AMI is available in both AZs
D) Make sure the maximum size of the Auto Scaling Group is greater than 4

ANSWER23:


B

NOTES23: AZs

Reference23: AZs

Question24: You have been asked to leverage Amazon VPC BC2 and SOS to implement an application that submits and receives millions of messages per second to a message queue. You want to ensure your application has sufficient bandwidth between your EC2 instances and SQS.
Which option will provide (he most scalable solution for communicating between the application and SOS?

A) Ensure the application instances are properly configured with an Elastic Load Balancer
B) Ensure the application instances are launched in private subnets with the EBS-optimized option enabled
C) Ensure the application instances are launched in public subnets with the associate-publicIP-address=true option enabled
D) Launch application instances in private subnets with an Auto Scaling group and Auto Scaling triggers configured to watch the SOS queue size

ANSWER24:


C

NOTES24: SQS

Reference24: SQS

Question25: You have identified network throughput as a bottleneck on your ml small EC2 instance when uploading data Into Amazon S3 In the same region. How do you remedy this situation?

A) Add an additional ENI
B) Change to a larger Instance
C) Use DirectConnect between EC2 and S3
D) Use EBS PIOPS on the local volume

ANSWER25:


B

NOTES25: EC2 instances

Reference25: EC2 Best Practices

Question 26: When attached to an Amazon VPC which two components provide connectivity with external networks? Choose 2 answers

A) Elastic IPS (EIP)
B) NAT Gateway (NAT)
C) Internet Gateway {IGW)
D) Virtual Private Gateway (VGW)

ANSWER26:

C. D.

NOTES26: IGW and VGW
Reference26: IGW – VGW

Question 27: Your application currently leverages AWS Auto Scaling to grow and shrink as load Increases’ decreases and has been performing well Your marketing team expects a steady ramp up in traffic to follow an upcoming campaign that will result in a 20x growth in traffic over 4 weeks Your forecast for the approximate number of Amazon EC2 instances necessary to meet the peak demand is 175. What should you do to avoid potential service disruptions during the ramp up in traffic?

A) Ensure that you have pre-allocated 175 Elastic IP addresses so that each server will be able to obtain one as it launches
B) Check the service limits in Trusted Advisor and adjust as necessary so the forecasted count remains within limits
C) Change your Auto Scaling configuration to set a desired capacity of 175 prior to the launch of the marketing campaign
D) Pre-warm your Elastic Load Balancer to match the requests per second anticipated during peak demand prior to the marketing campaign
ANSWER: 

D.

NOTES: Pre-warm your Elastic Load Balancer to match the requests per second anticipated during peak demand prior to the marketing campaign
Reference: AWS Auto Scaling

Question 28: You have an Auto Scaling group associated with an Elastic Load Balancer (ELB). You have noticed that instances launched via the Auto Scaling group are being marked unhealthy due to an ELB health check, but these unhealthy instances are not being terminated. What do you need to do to ensure trial instances marked unhealthy by the ELB will be terminated and replaced?

A) Change the thresholds set on the Auto Scaling group health check
B) Add an Elastic Load Balancing health check to your Auto Scaling group
C) Increase the value for the Health check interval set on the Elastic Load Balancer
D) Change the health check set on the Elastic Load Balancer to use TCP rather than HTTP checks
ANSWER: 

B.

NOTES: Add an Elastic Load Balancing Health Check to your Auto Scaling GroupBy default, an Auto Scaling group periodically reviews the results of EC2 instance status to determine the health state of each instance. However, if you have associated your Auto Scaling group with an Elastic Load Balancing load balancer, you can choose to use the Elastic Load Balancing health check. In this case, Auto Scaling determines the health status of your instances by checking the results of both the EC2 instance status check and the Elastic Load Balancing instance health check.
Reference:  AWS ELB

Question 29: Which two AWS services provide out-of-the-box user configurable automatic backup-as-a-service and backup rotation options? Choose 2 answers

A) Amazon S3
B) Amazon RDS
C) Amazon EBS
D) Amazon Redshift
ANSWER:

C. D.

NOTES: EBS and Redshift
Reference: EBS and Redshift
ReferenceUrl: EBS and Redshift

Question 30: An organization has configured a VPC with an Internet Gateway (IGW). pairs of public and private subnets (each with one subnet per Availability Zone), and an Elastic Load Balancer (ELB) configured to use the public subnets The application s web tier leverages the ELB. Auto Scaling and a mum-AZ RDS database instance The organization would like to eliminate any potential single points of failure in this design. What step should you take to achieve this organization’s objective?

A) Nothing, there are no single points of failure in this architecture.
B) Create and attach a second IGW to provide redundant internet connectivity.
C) Create and configure a second Elastic Load Balancer to provide a redundant load balancer.
D) Create a second multi-AZ RDS instance in another Availability Zone and configure replication to provide a redundant database.

ANSWER

C.

NOTES: Create and configure a second Elastic Load Balancer to provide a redundant load balancer.

Reference: ELB

Question 31: Which of the following are characteristics of Amazon VPC subnets? Choose 2 answers

A) Each subnet maps to a single Availability Zone
B) A CIDR block mask of /25 is the smallest range supported
C) Instances in a private subnet can communicate with the internet only if they have an Elastic IP.
D) By default, all subnets can route between each other, whether they are private or public
E) V Each subnet spans at least 2 Availability zones to provide a high-availability environment
ANSWER

C. E.

NOTES: VPC
Reference: VPC

Question 32: You are creating an Auto Scaling group whose Instances need to insert a custom metric into CloudWatch. Which method would be the best way to authenticate your CloudWatch PUT request?

A) Create an IAM role with the Put MetricData permission and modify the Auto Scaling launch configuration to launch instances in that role
B) Create an IAM user with the PutMetricData permission and modify the Auto Scaling launch configuration to inject the userscredentials into the instance User Data
C) Modify the appropriate Cloud Watch metric policies to allow the Put MetricData permission to instances from the Auto Scaling group
D) Create an IAM user with the PutMetricData permission and put the credentials in a private repository and have applications on the server pull the credentials as needed

ANSWER: 

B.

NOTES: Create an IAM user with the PutMetricData permission and modify the Auto Scaling launch configuration to inject the userscredentials into the instance User Data

Reference: IAM

Question 33: When an EC2 instance that is backed by an S3-based AMI Is terminated, what happens to the data on me root volume?

A) Data is automatically saved as an E8S volume.
B) Data is automatically saved as an ESS snapshot.
C) Data is automatically deleted.
D) Data is unavailable until the instance is restarted.
ANSWER: 

D.

NOTES: Data is unavailable until the instance is restarted.
Reference: AWS EC2
ReferenceUrl: AWS EC2 S3-based AMI

Question 34: You have a web application leveraging an Elastic Load Balancer (ELB) In front of the web servers deployed using an Auto Scaling Group Your database is running on Relational Database Service (RDS) The application serves out technical articles and responses to them in general there are more views of an article than there are responses to the article. On occasion, an article on the site becomes extremely popular resulting in significant traffic Increases that causes the site to go down. What could you do to help alleviate the pressure on the infrastructure while maintaining availability during these events? Choose 3 answers

A) Leverage CloudFront for the delivery of the articles.
B) Add RDS read-replicas for the read traffic going to your relational database
C) Leverage ElastiCache for caching the most frequently used data.
D) Use SOS to queue up the requests for the technical posts and deliver them out of the queue.
E) Use Route53 health checks to fail over to an S3 bucket for an error page.

ANSWER: 

A. C. E

NOTES: Leverage CloudFront, ElastiCache, Route53
Reference: CloudFront, ElastiCache, Route53

Question 35: The majority of your Infrastructure is on premises and you have a small footprint on AWS Your company has decided to roll out a new application that is heavily dependent on low latency connectivity to LOAP for authentication Your security policy requires minimal changes to the company’s existing application user management processes. What option would you implement to successfully launch this application1?

A) Create a second, independent LOAP server in AWS for your application to use for authentication
B) Establish a VPN connection so your applications can authenticate against your existing on-premises LDAP servers
C) Establish a VPN connection between your data center and AWS create a LDAP replica on AWS and configure your application to use the LDAP replica for authentication
D) Create a second LDAP domain on AWS establish a VPN connection to establish a trust relationship between your new and existing domains and use the new domain for authentication

ANSWER: 

D.

NOTES: Trust Relationship
Reference: Trust Relationship

SOURCES:

Djamga DevOps  Youtube Channel:

Prepare for Your AWS Certification Exam

2- GoCertify

SYSOPS AND SYSADMIN NEWS

SYSADMIN – SYSOPS RESOURCES

I WANT TO BECOME A SYSADMIN

This is a common topic that has been asked multiple times.

Professional/Non-technical

Sysadmin Utilities

Security

Linux

Microsoft / Windows Server

Virtualization

MacOS (formerly OSX) and Apple iOS

Google ChromeOS

Backup and Storage

Networking

Monitoring

  • Because your network and infrastructure can’t be a black box

Business and Standards Compliance

Major Vulnerabilities

Podcasts

Documentation

Testimonials:

I was initially nervous about this exam compared to SAA-C02, due to the practical labs. However, they turned out to be really easy with lots of time to fumble about, delete & recreate resources.

My labs:

Create S3 buckets, set access logs, set default encryption with KMS and create a bunch of lifecycle policies

Create a VPC with public/private subnets, create SGs, create & send flow logs to an S3 bucket.

Connect Lambda to a VPC, use RDS proxy to connect to an RDS Database. Select correct execution role for the Lambda.

Exam lab experience

I did not have any negative experiences with the lab environment (I heard a lot of horror stories), however I did take the exam at a testing center.

When you register for your SOA-C02, you gain access (via Pearson VUE E-mail) to a free sample exam lab at Login – OneLearn Training Management System – Skillable – this is the exact same testing environment you will have during the actual exam. I highly recommend you do this, especially if you’re doing the exam from home – any issues you have with the testing environment like laggy interface, copy/paste issues, etc you’ll probably also have during the exam.

Study resources

My study resources were:

Adrian Cantrill’s course

Jon Bonso’s (TutorialDojo) Practice Exams

uacantril’s courses are the best, most high quality courses I’ve ever taken for any subject.

Since I’ve done the SAA-C02 course before doing the SOA-C02 course, I was able to easily skip the shared lessons & demos (there heavy overlap between these two exams) and focus on the SOA-C02 specific topics.

uTutorials_Dojo’s practice exams are 10/10 as preparation material. They were a bit more tricky (in a ‘gotcha’ kind of way) compared to the exam questions, but they were very close to the real thing.

Study methodology

My study plan was as follows:

Study Time: 7:00-9:00 (morning) Mon-Fri, which included:

Going through Adrian’s course

Detailed notes in markdown

Doing potential exam labs in AWS console

Reading AWS official documentation (in case something is not clear)

Review Notes regularly (once course material finished)

Practice Exams

Doing exams in review mode

Delving deeper into topics I was lacking in

This was the plan, but I turned out to be somewhat inconsistent, taking the exam 3 months later than planned due to being a new father and not focusing on just one thing (also did some Python learning during the same period). But, still a pass!

Source: r/AWSCertification

Top 20 AWS Certified Associate SysOps Administrator Practice Quiz - Questions and Answers Dumps

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