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AWS Certified Data Engineer Associate DEA-C01 Practice Question

A company runs a daily Apache Spark job that converts 500 GB of CSV files in Amazon S3 to Parquet. The job currently runs on an Amazon EMR cluster that remains active for the rest of the day, incurring charges even when it is idle. The data engineering team must reduce processing costs but keep similar performance and avoid managing long-running infrastructure. Which approach meets these requirements MOST cost-effectively?

  • Convert the cluster to Amazon EKS and run the Spark job as a Kubernetes pod using EMR on EKS.

  • Re-implement the workload as an Amazon EMR Serverless application and submit the Spark job on a daily schedule.

  • Keep the EMR cluster but enable EMR managed scaling with Spot Instances for task nodes.

  • Move the job to an AWS Glue Spark ETL job that runs on a schedule with a fixed DPU allocation.

AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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