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 Glue job bookmarks maintain state about previously processed data, allowing a job to skip files it has already loaded. Enabling bookmarks turns the load into an incremental batch ingestion and sharply reduces the amount of data that must be read and written each night. Merely increasing DPUs raises cost but still processes all historical files, S3 Transfer Acceleration affects network transfer rather than eliminating redundant reads, and rewriting the job as a Python shell would remove Spark parallelism and require code changes without addressing the root cause.
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What are AWS Glue job bookmarks?
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How does enabling job bookmarks differ from increasing DPUs?
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What is S3 Transfer Acceleration, and why is it not suitable here?
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What are AWS Glue job bookmarks?
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How does enabling AWS Glue job bookmarks reduce processing time?
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What alternatives to AWS Glue job bookmarks exist for incremental data processing?
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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