DEA-C01 practice questions 131–140

1. Question 131 · 313 in bank

   A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform. The company wants to minimize the effort and time required to incorporate third-party datasets. Which solution will meet these requirements with the LEAST operational overhead?

   • AUse API calls to access and integrate third-party datasets from AWS Data Exchange.
   • BUse API calls to access and integrate third-party datasets from AWS DataSync.
   • CUse Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
   • DUse Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).

   Show answer

   Answer: A

   Explanation

   AWS DataSync is primarily used for data transfer services designed to simplify, automate, and accelerate moving data between on-premises storage systems and AWS storage services, as well as between different AWS storage services. Its primary role is not for accessing third-party datasets but for efficiently transferring large volumes of data. In contrast, AWS Data Exchange is designed specifically for discovering and subscribing to third-party data in the cloud, providing direct API access to these datasets, which aligns perfectly with the company's need to integrate this data into their recommendation systems with minimal overhead.

   Source: Adapted from community discussion — KelvinPun

   References

   • AWS documentation
   • AWS documentation

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2. Question 132 · 313 in bank

   A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information. The data engineer must identify and remove duplicate information from the legacy application data. Which solution will meet these requirements with the LEAST operational overhead?

   • AWrite a custom extract, transform, and load (ETL) job in Python. Use the DataFrame.drop_duplicates() function by importing the Pandas library to perform data deduplication.
   • BWrite an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.
   • CWrite a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
   • DWrite an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

   Show answer

   Answer: B

   Explanation

   B. https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html "Find matches Finds duplicate records in the source data. You teach this machine learning transform by labeling example datasets to indicate which rows match. The machine learning transform learns which rows should be matches the more you teach it with example labeled data."

   Source: Adapted from community discussion — GiorgioGss

   References

   • AWS documentation

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3. Question 133 · 313 in bank

   Select 2

   A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3. Which actions will provide the FASTEST queries? (Choose two.)

   • AUse gzip compression to compress individual files to sizes that are between 1 GB and 5 GB.
   • BUse a columnar storage file format.
   • CPartition the data based on the most common query predicates.
   • DSplit the data into files that are less than 10 KB.
   • EUse file formats that are not splittable.

   Show answer

   Answer: BC

   Explanation

   B. Use a columnar storage file format: This is an excellent approach. Columnar storage formats like Parquet and ORC are highly recommended for use with Redshift Spectrum. They store data in columns, which allows Spectrum to scan only the needed columns for a query, significantly improving query performance and reducing the amount of data scanned. C. Partition the data based on the most common query predicates: Partitioning data in S3 based on commonly used query predicates (like date, region, etc.) allows Redshift Spectrum to skip large portions of data that are irrelevant to a particular query. This can lead to substantial performance improvements, especially for large datasets.

   Source: Adapted from community discussion — rralucard_

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation

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4. Question 134 · 313 in bank

   Select 2

   A company uses Amazon RDS to store transactional data. The company runs an RDS DB instance in a private subnet. A developer wrote an AWS Lambda function with default settings to insert, update, or delete data in the DB instance. The developer needs to give the Lambda function the ability to connect to the DB instance privately without using the public internet. Which combination of steps will meet this requirement with the LEAST operational overhead? (Choose two.)

   • ATurn on the public access setting for the DB instance.
   • BUpdate the security group of the DB instance to allow only Lambda function invocations on the database port.
   • CConfigure the Lambda function to run in the same subnet that the DB instance uses.
   • DAttach the same security group to the Lambda function and the DB instance. Include a self-referencing rule that allows access through the database port.
   • EUpdate the network ACL of the private subnet to include a self-referencing rule that allows access through the database port.

   Show answer

   Answer: CD

   Explanation

   This solution only modifies the inbound rules of the security group of the DB instance, but it does not modify the outbound rules of the security group of the Lambda function. Additionally, this solution does not facilitate a private connection from the Lambda function to the DB instance, hence, the Lambda function would still need to use the public internet to access the DB instance. Therefore, this option does not fulfill the requirements.

   Source: Adapted from community discussion — Alagong

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation

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5. Question 135 · 313 in bank

   A company has a frontend ReactJS website that uses Amazon API Gateway to invoke REST APIs. The APIs perform the functionality of the website. A data engineer needs to write a Python script that can be occasionally invoked through API Gateway. The code must return results to API Gateway. Which solution will meet these requirements with the LEAST operational overhead?

   • ADeploy a custom Python script on an Amazon Elastic Container Service (Amazon ECS) cluster.
   • BCreate an AWS Lambda Python function with provisioned concurrency.
   • CDeploy a custom Python script that can integrate with API Gateway on Amazon Elastic Kubernetes Service (Amazon EKS).
   • DCreate an AWS Lambda function. Ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events.

   Show answer

   Answer: B

   Explanation

   B. AWS Lambda functions can be easily integrated with Amazon API Gateway to create RESTful APIs. This integration allows API Gateway to directly invoke the Lambda function when the API endpoint is hit.

   Source: Adapted from community discussion — rralucard_

   References

   • AWS documentation

   Next question

6. Question 136 · 313 in bank

   A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs. The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account. Which solution will meet these requirements?

   • ACreate a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
   • BCreate a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
   • CCreate a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
   • DCreate a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.

   Show answer

   Answer: D

   Explanation

   1. **Cross-Account Access:** - AWS Documentation: [Cross-Account Access] https://docs.aws.amazon.com/IAM/latest/UserGuide/tutorial_cross-account-with-roles.html - This documentation provides detailed instructions on how to set up cross-account access using IAM roles and trust policies, which is essential for allowing CloudWatch Logs in one AWS account to put data into a Kinesis Data Stream in another AWS account. 2. **Configuring CloudWatch Logs Subscription Filters:** - AWS Documentation: [Subscription Filters for Amazon CloudWatch Logs] https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html - This documentation explains how to create subscription filters for CloudWatch Logs, which enable you to route log data to various destinations, including Kinesis Data Streams. Placing the subscription filter in the production AWS account ensures that only the relevant security logs are sent to the Kinesis Data Stream in the security AWS account.

   Source: Adapted from community discussion — certplan

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation

   Next question

7. Question 137 · 313 in bank

   A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes. A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake. Which solution will capture the changed data MOST cost-effectively?

   • ACreate an AWS Lambda function to identify the changes between the previous data and the current data. Configure the Lambda function to ingest the changes into the data lake.
   • BIngest the data into Amazon RDS for MySQL. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.
   • CUse an open source data lake format to merge the data source with the S3 data lake to insert the new data and update the existing data.
   • DIngest the data into an Amazon Aurora MySQL DB instance that runs Aurora Serverless. Use AWS Database Migration Service (AWS DMS) to write the changed data to the data lake.

   Show answer

   Answer: C

   Explanation

   Relative to cost, here are docs for the reason for option C: https://docs.aws.amazon.com/AmazonS3/latest/dev/Welcome.html https://aws.amazon.com/blogs/big-data/ https://docs.aws.amazon.com/glue/latest/dg/welcome.html https://docs.aws.amazon.com/emr/ Here are docs for reasons the others are not correct: https://aws.amazon.com/lambda/pricing/ https://aws.amazon.com/rds/pricing/ https://aws.amazon.com/dms/pricing/

   Source: Adapted from community discussion — certplan

   References

   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation
   • AWS documentation

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8. Question 138 · 313 in bank

   Select 2

   A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table. The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time. Which solutions will meet these requirements? (Choose two.)

   • ACreate an AWS Glue partition index. Enable partition filtering.
   • BBucket the data based on a column that the data have in common in a WHERE clause of the user query.
   • CUse Athena partition projection based on the S3 bucket prefix.
   • DTransform the data that is in the S3 bucket to Apache Parquet format.
   • EUse the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.

   Show answer

   Answer: AC

   Explanation

   https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/ Optimizing Partition Processing using partition projection Processing partition information can be a bottleneck for Athena queries when you have a very large number of partitions and aren’t using AWS Glue partition indexing. You can use partition projection in Athena to speed up query processing of highly partitioned tables and automate partition management. Partition projection helps minimize this overhead by allowing you to query partitions by calculating partition information rather than retrieving it from a metastore. It eliminates the need to add partitions’ metadata to the AWS Glue table.

   Source: Adapted from community discussion — rralucard_

   References

   • AWS documentation

   Next question

9. Question 139 · 313 in bank

   A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant. Which solution will meet these requirements with the LEAST operational overhead?

   • AUse an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.
   • BUse Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.
   • CUse an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.
   • DUse Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.

   Show answer

   Answer: D

   Explanation

   D. Amazon Managed Service for Apache Flink for Time-Based Analytics over 30 Minutes: This option correctly identifies the use of Amazon Managed Service for Apache Flink for performing time-based analytics over a window of up to 30 minutes. Apache Flink is adept at handling such scenarios, providing capabilities for complex event processing, time-windowed aggregations, and maintaining state over time. This option would offer high fault tolerance and minimal operational overhead due to the managed nature of the service.

   Source: Adapted from community discussion — rralucard_

   References

   • AWS documentation

   Next question

10. Question 140 · 313 in bank

    A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage. Which solution will meet these requirements with the LEAST operational overhead?

    • ACreate snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.
    • BCreate new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
    • CChange the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.
    • DUse AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.

    Show answer

    Answer: C

    Explanation

    Option C: Check section under "To modify an Amazon EBS volume using the AWS Management Console“ in GiorgioGss's link Amazon EBS Elastic Volumes enable you to modify your volume type from gp2 to gp3 without detaching volumes or restarting instances (requirements for modification), which means that there are no interruptions to your applications during modification.

    Source: Adapted from community discussion — fceb2c1

    References

    • AWS documentation
    • AWS documentation

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DEA-C01 practice, question 131. 313 questions available.