QUESTION: 17

A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

Configure AWS Glue triggers to run the ETL jobs every hour.
Use AWS Glue DataBrew to clean and prepare the data for analytics.
Use AWS Lambda functions to schedule and run the ETL jobs every hour.
Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.
E. Use the Redshift Data API to load transformed data into Amazon Redshift.

Answer(s): A,D

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QUESTION: 18

A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.
Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.
Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.
Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

Answer(s): B

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QUESTION: 19

A data engineer must orchestrate a series of Amazon Athena queries that will run every day. Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.
Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.
Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.

Answer(s): A,B

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QUESTION: 20

A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?

AWS Glue
Amazon EMR
AWS Lambda
Amazon Redshift

Answer(s): B

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Free AWS Certified Data Engineer - Associate DEA-C01 Exam Braindumps (page: 5)

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QUESTION: 20