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AWS Certified Data Engineer Associate DEA-C01 Practice Question

An analytics team stores 2 TB of GZIP-compressed JSON click-stream data in Amazon S3. The data engineer needs a web notebook to interactively run PySpark, inspect samples, and write partitioned Parquet back to S3, without managing clusters and paying only when code executes. Which AWS approach satisfies these goals?

  • Launch an Amazon EMR cluster with Apache Zeppelin notebooks and configure the cluster to auto-terminate when idle.

  • Create an Athena for Apache Spark notebook and use the interactive session to transform and write the data.

  • Use AWS Glue DataBrew to profile the JSON files and export the results as Parquet.

  • Build an on-demand AWS Glue PySpark job that is started by AWS Step Functions when transformation is required.

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