DEA-C01 practice questions 191–200

1. Question 191 · 313 in bank

   An online retail company stores Application Load Balancer (ALB) access logs in an Amazon S3 bucket. The company wants to use Amazon Athena to query the logs to analyze traffic patterns. A data engineer creates an unpartitioned table in Athena. As the amount of the data gradually increases, the response time for queries also increases. The data engineer wants to improve the query performance in Athena. Which solution will meet these requirements with the LEAST operational effort?

   • ACreate an AWS Glue job that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.
   • BCreate an AWS Glue crawler that includes a classifier that determines the schema of all ALB access logs and writes the partition metadata to AWS Glue Data Catalog.
   • CCreate an AWS Lambda function to transform all ALB access logs. Save the results to Amazon S3 in Apache Parquet format. Partition the metadata. Use Athena to query the transformed data.
   • DUse Apache Hive to create bucketed tables. Use an AWS Lambda function to transform all ALB access logs.

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   Answer: B

   Explanation

   Creating an AWS Glue crawler (Option B) is the most straightforward and least operationally intensive approach to automatically determine the schema, partition the data, and keep the AWS Glue Data Catalog updated. This ensures Athena queries are optimized without requiring extensive manual management or additional processing steps.

   Source: Adapted from community discussion — PGGuy

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2. Question 192 · 313 in bank

   A company has a business intelligence platform on AWS. The company uses an AWS Storage Gateway Amazon S3 File Gateway to transfer files from the company's on-premises environment to an Amazon S3 bucket. A data engineer needs to setup a process that will automatically launch an AWS Glue workflow to run a series of AWS Glue jobs when each file transfer finishes successfully. Which solution will meet these requirements with the LEAST operational overhead?

   • ADetermine when the file transfers usually finish based on previous successful file transfers. Set up an Amazon EventBridge scheduled event to initiate the AWS Glue jobs at that time of day.
   • BSet up an Amazon EventBridge event that initiates the AWS Glue workflow after every successful S3 File Gateway file transfer event.
   • CSet up an on-demand AWS Glue workflow so that the data engineer can start the AWS Glue workflow when each file transfer is complete.
   • DSet up an AWS Lambda function that will invoke the AWS Glue Workflow. Set up an event for the creation of an S3 object as a trigger for the Lambda function.

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   Answer: B

   Explanation

   Using EventBridge directly to trigger the AWS Glue workflow upon S3 events is straightforward and leverages AWS's event-driven architecture, requiring minimal maintenance.

   Source: Adapted from community discussion — tgv

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3. Question 193 · 313 in bank

   A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data. The company uses an Amazon Redshift cluster for a data warehouse. An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster. A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs. Which combination of steps will meet these requirements? (Choose two.)

   • AConfigure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database.
   • BConfigure Amazon Redshift Spectrum to query live transactional data that is in the PostgreSQL database.
   • CSchedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.
   • DSchedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Redshift Spectrum to access historical data from S3 Glacier Flexible Retrieval.
   • ECreate a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.

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   Answer: A

   Explanation

   Option A (A): Configuring Amazon Redshift Federated Query allows Redshift to directly query the live transactional data in the PostgreSQL database without needing to import it. This ensures that you can access the most recent live data efficiently. Option C (C): Scheduling a monthly job to copy data older than 15 months to Amazon S3 and then using Amazon Redshift Spectrum to access this historical data provides a cost-effective way to manage storage. This ensures that only the most recent 15 months of data are kept in Amazon Redshift, reducing storage costs. The historical data is still accessible via Redshift Spectrum for analytics queries.

   Source: Adapted from community discussion — lalitjhawar

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4. Question 194 · 313 in bank

   A manufacturing company has many IoT devices in facilities around the world. The company uses Amazon Kinesis Data Streams to collect data from the devices. The data includes device ID, capture date, measurement type, measurement value, and facility ID. The company uses facility ID as the partition key. The company's operations team recently observed many WriteThroughputExceeded exceptions. The operations team found that some shards were heavily used but other shards were generally idle. How should the company resolve the issues that the operations team observed?

   • AChange the partition key from facility ID to a randomly generated key.
   • BIncrease the number of shards.
   • CArchive the data on the producer's side.
   • DChange the partition key from facility ID to capture date.

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   Answer: A

   Explanation

   The correct answer is **A. Change the partition key from facility ID to a randomly generated key.** Amazon Kinesis Data Streams uses the partition key that you specify to segregate the data records in the stream into shards. If the company uses the facility ID as the partition key, and if some facilities produce more data than others, then the data will be unevenly distributed across the shards. This can lead to some shards being heavily used while others are idle, and can cause `WriteThroughputExceeded` exceptions. By changing the partition key to a randomly generated key, the data records are more likely to be evenly distributed across all the shards, which can help to avoid the issue of some shards being heavily used and others being idle. This solution requires the least operational overhead and does not involve increasing costs (as in option B), archiving data (which might not be desirable or feasible, as in option C), or changing to a partition key that might also lead to uneven distribution (as in option D).

   Source: Adapted from community discussion — bakarys

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5. Question 195 · 313 in bank

   A data engineer wants to improve the performance of SQL queries in Amazon Athena that run against a sales data table. The data engineer wants to understand the execution plan of a specific SQL statement. The data engineer also wants to see the computational cost of each operation in a SQL query. Which statement does the data engineer need to run to meet these requirements?

   • AEXPLAIN SELECT * FROM sales;
   • BEXPLAIN ANALYZE FROM sales;
   • CEXPLAIN ANALYZE SELECT * FROM sales;
   • DEXPLAIN FROM sales;

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   Answer: C

   Explanation

   A - Only partially meets the requirements as it does not include computational costs. B - Incorrect syntax and does not meet the requirements. C - Fully meets the requirements by providing both the execution plan and the computational costs. D - Incorrect syntax and does not meet the requirements.

   Source: Adapted from community discussion — tgv

   References

   • AWS documentation

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6. Question 196 · 313 in bank

   A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time. Which solution will run the Glue jobs in the MOST cost-effective way?

   • AChoose the FLEX execution class in the Glue job properties.
   • BUse the Spot Instance type in Glue job properties.
   • CChoose the STANDARD execution class in the Glue job properties.
   • DChoose the latest version in the GlueVersion field in the Glue job properties.

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   Answer: A

   Explanation

   The FLEX execution class leverages spare capacity within the AWS infrastructure to run Glue jobs at a discounted price compared to the standard execution class. Since the data engineer doesn't have specific time constraints, utilizing spare capacity is ideal for cost savings. Today's date its a checkbox in order to spare capacity and will mean we dont know when is going to finish, which is recommended to increase a timeout

   Source: Adapted from community discussion — pypelyncar

   References

   • AWS documentation

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7. Question 197 · 313 in bank

   A company plans to provision a log delivery stream within a VPC. The company configured the VPC flow logs to publish to Amazon CloudWatch Logs. The company needs to send the flow logs to Splunk in near real time for further analysis. Which solution will meet these requirements with the LEAST operational overhead?

   • AConfigure an Amazon Kinesis Data Streams data stream to use Splunk as the destination. Create a CloudWatch Logs subscription filter to send log events to the data stream.
   • BCreate an Amazon Kinesis Data Firehose delivery stream to use Splunk as the destination. Create a CloudWatch Logs subscription filter to send log events to the delivery stream.
   • CCreate an Amazon Kinesis Data Firehose delivery stream to use Splunk as the destination. Create an AWS Lambda function to send the flow logs from CloudWatch Logs to the delivery stream.
   • DConfigure an Amazon Kinesis Data Streams data stream to use Splunk as the destination. Create an AWS Lambda function to send the flow logs from CloudWatch Logs to the data stream.

   Show answer

   Answer: B

   Explanation

   Creating an Amazon Kinesis Data Firehose delivery stream to use Splunk as the destination and creating a CloudWatch Logs subscription filter to send log events to the delivery stream would meet these requirements with the least operational overhead. Amazon Kinesis Data Firehose is the easiest way to reliably load streaming data into data lakes, data stores, and analytics services. It can capture, transform, and deliver streaming data to Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, generic HTTP endpoints, and service providers like Splunk. CloudWatch Logs subscription filters allow you to send real-time log events to Kinesis Data Firehose and are ideal for scenarios where you want to forward the logs to other services for further analysis. Options A and D involve Kinesis Data Streams, which would require additional management and operational overhead. Option C involves creating a Lambda function, which also adds operational overhead. Therefore, option B is the best choice.

   Source: Adapted from community discussion — bakarys

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8. Question 198 · 313 in bank

   A company has a data lake on AWS. The data lake ingests sources of data from business units. The company uses Amazon Athena for queries. The storage layer is Amazon S3 with an AWS Glue Data Catalog as a metadata repository. The company wants to make the data available to data scientists and business analysts. However, the company first needs to manage fine-grained, column-level data access for Athena based on the user roles and responsibilities. Which solution will meet these requirements?

   • ASet up AWS Lake Formation. Define security policy-based rules for the users and applications by IAM role in Lake Formation.
   • BDefine an IAM resource-based policy for AWS Glue tables. Attach the same policy to IAM user groups.
   • CDefine an IAM identity-based policy for AWS Glue tables. Attach the same policy to IAM roles. Associate the IAM roles with IAM groups that contain the users.
   • DCreate a resource share in AWS Resource Access Manager (AWS RAM) to grant access to IAM users.

   Show answer

   Answer: A

   Explanation

   Correct Solution: A. Set up AWS Lake Formation. Define security policy-based rules for the users and applications by IAM role in Lake Formation. Explanation: AWS Lake Formation: This service simplifies and automates the process of securing and managing data lakes. It allows you to define fine-grained access control policies at the database, table, and column levels. Security Policy-Based Rules: Lake Formation allows you to create policies that specify which users or roles have access to specific data, including column-level access controls. This makes it easier to manage access based on roles and responsibilities.

   Source: Adapted from community discussion — Ja13

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9. Question 199 · 313 in bank

   A company has developed several AWS Glue extract, transform, and load (ETL) jobs to validate and transform data from Amazon S3. The ETL jobs load the data into Amazon RDS for MySQL in batches once every day. The ETL jobs use a DynamicFrame to read the S3 data. The ETL jobs currently process all the data that is in the S3 bucket. However, the company wants the jobs to process only the daily incremental data. Which solution will meet this requirement with the LEAST coding effort?

   • ACreate an ETL job that reads the S3 file status and logs the status in Amazon DynamoDB.
   • BEnable job bookmarks for the ETL jobs to update the state after a run to keep track of previously processed data.
   • CEnable job metrics for the ETL jobs to help keep track of processed objects in Amazon CloudWatch.
   • DConfigure the ETL jobs to delete processed objects from Amazon S3 after each run.

   Show answer

   Answer: B

   Explanation

   The solution that will meet this requirement with the least coding effort is Option B: Enable job bookmarks for the ETL jobs to update the state after a run to keep track of previously processed data. AWS Glue job bookmarks help ETL jobs to keep track of data that has already been processed during previous runs. By enabling job bookmarks, the ETL jobs can skip the processed data and only process the new, incremental data. This feature is designed specifically for this use case and requires minimal coding effort. Options A, C, and D would require additional coding and operational effort. Option A would require creating a new ETL job and managing a DynamoDB table. Option C would involve setting up job metrics and CloudWatch, which doesn’t directly address processing incremental data. Option D would involve deleting data from S3 after processing, which might not be desirable if the original data needs to be retained. Therefore, Option B is the most suitable solution.

   Source: Adapted from community discussion — bakarys

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10. Question 200 · 313 in bank

    An online retail company has an application that runs on Amazon EC2 instances that are in a VPC. The company wants to collect flow logs for the VPC and analyze network traffic. Which solution will meet these requirements MOST cost-effectively?

    • APublish flow logs to Amazon CloudWatch Logs. Use Amazon Athena for analytics.
    • BPublish flow logs to Amazon CloudWatch Logs. Use an Amazon OpenSearch Service cluster for analytics.
    • CPublish flow logs to Amazon S3 in text format. Use Amazon Athena for analytics.
    • DPublish flow logs to Amazon S3 in Apache Parquet format. Use Amazon Athena for analytics.

    Show answer

    Answer: D

    Explanation

    Publishing flow logs to Amazon S3 in Apache Parquet format and using Amazon Athena for analytics (D) is the most cost-effective solution. This approach minimizes storage costs due to the efficient compression of Parquet, and optimizes query performance and cost in Athena due to the reduced data size and optimized columnar storage.

    Source: Adapted from community discussion — PGGuy

    References

    • AWS documentation
    • AWS documentation

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DEA-C01 practice, question 191. 313 questions available.