AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data engineering team runs a nightly Apache Spark ETL job on a 10-node Amazon EMR cluster. The job finishes in 40 minutes, but the cluster sits idle all day. Input volume sometimes spikes, so the job needs extra executors. The team wants to cut costs without refactoring code and must keep using Spot Instances. Which solution meets these requirements with the LEAST operational effort?
Enable EMR managed scaling and set the cluster to terminate automatically after 30 minutes of idle time.
Re-platform the workload to Amazon EMR on EKS and use Karpenter to auto-scale Spot worker nodes.
Replace the cluster with Amazon EMR Serverless and specify a maximum capacity large enough for peak workloads.
Migrate the Spark application to AWS Glue and schedule a Glue Spark job that runs on-demand workers.
Enabling EMR managed scaling lets Amazon EMR automatically add or remove Spot Instance core and task nodes while a step is running, providing capacity when data spikes occur and shrinking the cluster when demand falls. Turning on the automatic termination timeout shuts the cluster down after the job completes and the cluster is idle, eliminating the daily idle charges. This approach keeps the existing Spark code and EMR deployment while requiring only configuration changes, so it involves less operational effort than migrating to EMR Serverless, AWS Glue, or EMR on EKS.
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AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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