Crucial Exams

AWS Certified Data Engineer Associate DEA-C01 Practice Question

An ecommerce company stores 5 GB of JSON click-stream records in an Amazon S3 prefix each day. The analytics team must convert the data to compressed Parquet, partition the output by event_date, and automatically adapt when new fields appear. The transformation must finish within one hour and require the least ongoing operational effort. Which solution meets these requirements?

  • Spin up an on-demand Amazon EMR cluster daily, run a PySpark script to convert the JSON to Parquet partitions, and terminate the cluster when the job completes.

  • Configure an Amazon S3 event to invoke an AWS Lambda function that processes each object and writes the transformed data to Parquet partitions in another bucket.

  • Create an AWS Glue Spark ETL job that reads the JSON data into a DynamicFrame and writes compressed, event_date-partitioned Parquet files back to Amazon S3.

  • Define an external table on the JSON files in Amazon Redshift Spectrum and schedule an hourly CTAS query that writes Parquet partitions to a different S3 prefix.

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