Crucial Exams

AWS Certified Data Engineer Associate DEA-C01 Practice Question

An analytics team runs ad hoc PySpark jobs on Amazon EMR. Each job iteratively processes a 40 GB Parquet data set stored in Amazon S3, rereading the files each time and incurring high latency and S3 request costs. Jobs launch only twice per week, making a long-running cluster too expensive. Which configuration will yield the fastest run time while minimizing total cost?

  • Create a persistent EC2 fleet with attached EBS volumes, populate them with the dataset, and mount the volumes as HDFS for all future EMR jobs.

  • Use EMRFS Consistent View and run the Spark job directly against the objects stored in Amazon S3.

  • Launch a transient EMR cluster and add an S3DistCp step to copy the input data to HDFS at startup; write final results back to S3 before the cluster terminates.

  • Enable Amazon S3 Select pushdown in Spark so each iteration reads only the required Parquet columns from S3.

AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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