AWS Certified Data Engineer Associate DEA-C01 Practice Question
A data engineering team needs to build a daily pipeline that reads raw transaction files from Amazon S3, performs multi-table joins, and applies user-defined Python functions to generate analytics datasets. The team wants to store the transformation logic as PySpark scripts in Git and run the code on a fully managed, serverless service without administering clusters. Which AWS service meets these requirements with the least operational overhead?
Create an AWS Glue job that runs the PySpark script
Schedule an Amazon Athena query with Amazon EventBridge
Invoke the PySpark script from an AWS Step Functions workflow
Use AWS Glue DataBrew to build the transformations visually
AWS Glue jobs natively execute PySpark or Scala scripts on a serverless Spark runtime. Engineers can author the code locally, commit it to version control, and reference the script in a Glue job. Glue automatically provisions and scales the compute, provides job bookmarking, and requires no cluster management.
Amazon Athena supports SQL, not PySpark, and would not run custom Spark code. AWS Glue DataBrew is a no-code visual tool and does not accept user-supplied scripts. AWS Step Functions can orchestrate workflows but does not itself execute PySpark; it would still require another compute service such as EMR or Glue, increasing complexity.
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How does AWS Glue execute PySpark scripts serverlessly?
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What is job bookmarking in AWS Glue?
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Why can't Amazon Athena run PySpark scripts?
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What is AWS Glue, and why is it suitable for this use case?
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What are PySpark scripts, and how are they used in AWS Glue?
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How does AWS Glue compare to AWS Athena and Glue DataBrew for ETL workflows?
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AWS Certified Data Engineer Associate DEA-C01
Data Operations and Support
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