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AWS Certified Data Engineer Associate DEA-C01 Practice Question

An ecommerce company stores raw clickstream events in Amazon S3 as date-partitioned Parquet files cataloged by AWS Glue. A data engineer must automate a daily SQL aggregation that joins about 3 TB of data and appends the results to another S3 bucket for downstream dashboards. The solution must automatically register the new dataset in the Data Catalog and minimize cost and operational overhead. Which solution meets these requirements?

  • Use AWS Step Functions to launch an on-demand Amazon EMR cluster running Hive, execute the aggregation job, copy the results to the target bucket, and then terminate the cluster.

  • Create an AWS Glue Spark ETL job triggered by EventBridge to perform the aggregation and write the results to the target bucket; schedule a separate Glue crawler to update the Data Catalog.

  • Load the source Parquet data into Amazon Redshift Serverless, schedule a query to create the aggregated table, and use UNLOAD to export the results back to the target S3 bucket.

  • Configure an Amazon EventBridge rule to run a scheduled Amazon Athena CTAS query that writes partitioned Parquet output to the target S3 bucket and automatically creates the corresponding table in the AWS Glue Data Catalog.

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