Crucial Exams

AWS Certified Data Engineer Associate DEA-C01 Practice Question

An e-commerce company transforms 2 TB of clickstream data stored in Amazon S3 every night by running a PySpark script that is version-controlled in an S3 path. Engineers want to invoke the job from a Jenkins pipeline through API calls, avoid managing any clusters, yet retain access to the Spark UI for detailed job troubleshooting. Which solution best satisfies these requirements?

  • Load the script into an Amazon Athena Spark notebook and invoke it by calling the StartQueryExecution API; view execution output in Athena's query editor for debugging.

  • Package the script in a Docker image and run it with AWS Batch on AWS Fargate; submit the job via the SubmitJob API; inspect the CloudWatch Logs stream for troubleshooting.

  • Create an AWS Glue Spark job that references the script in Amazon S3; trigger the job by calling the StartJobRun API from Jenkins; use the AWS Glue Spark UI to debug failed runs.

  • Provision an Amazon EMR cluster on EC2 each night and submit the script as a step by calling the AddJobFlowSteps API; access the Spark UI on the cluster's master node for troubleshooting; terminate the cluster after completion.

AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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