AWS Certified Data Engineer Associate DEA-C01 Practice Question

An e-commerce company stores daily sales files in the s3://sales/raw/ bucket with the pattern YYYY/MM/DD/sales.csv. A nightly AWS Glue Spark job loads the data into Amazon Redshift. Currently the job rereads every file and sometimes exceeds the 1-hour SLA. What configuration change will minimize processing time without rewriting code or increasing DPUs?

  • Enable job bookmarks for the Glue job so that it skips already processed files.

  • Increase the job's worker type to G.2X and set the number of workers to 20.

  • Convert the Spark job to a Python shell job that uses the Redshift Data API.

  • Enable S3 Transfer Acceleration on the sales bucket to reduce read latency.

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