DEA-C01 practice question: Transformation with AWS Glue (2 of 3)
Data Ingestion and Transformation A free question from our original DEA-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A public library system runs a Spark job in AWS Glue each night at 1:00 AM. The job joins circulation records with patron data and writes a summary table. It takes about 90 minutes on G.1X workers with the standard execution class, and nobody needs the output before the library's reporting starts at 7:00 AM.
The team wants to cut the nightly Glue cost without changing the summary logic. A run that starts a few minutes late is acceptable, but the output must still be ready before 7:00 AM.
Which TWO changes meet these requirements? (Choose two.)
Choose two answers.
AA. Change the job's execution class to Flex, which uses spare capacity at a lower price for non-urgent jobs that can wait.
BB. Move the job to G.8X workers and halve the worker count, so that each worker handles more data and the job finishes faster at about the same cost.
CC. Replace the Spark job with a Glue Python shell job so that it runs on a single small instance.
DD. Keep the standard workers and add a second identical job that runs at 3:00 AM, so that if the first run fails, the second run finishes the work.
EE. Turn on Glue auto scaling with a maximum worker count, so the job adds workers only while it has work to do.
Show the answer and explanation
Answers: A, E
A. Right: Correct. The Flex execution class runs on spare capacity at a lower price and is meant for non-urgent jobs that can tolerate a later start. It supports AWS Glue 3.0 and later jobs on G.1X or G.2X workers, and the 7:00 AM deadline leaves plenty of slack. Learn more: Configuring job properties for Spark jobs in AWS Glue (opens in a new tab)
B. Wrong: Larger workers cost more per hour, and halving the count does not keep the runtime or the cost the same. The option does not reduce cost and may slow the job down.
C. Wrong: A Python shell job runs single-process Python and does not provide the Spark engine that the join depends on. The job might not finish before 7:00 AM, and the summary logic would need a rewrite.
D. Wrong: A second run repeats the same compute when the first run succeeds, which raises the cost. The option adds spend instead of reducing it.
E. Right: Correct. With Auto Scaling, Glue removes idle executors and their workers and adds workers only when Spark requests them, up to the maximum you set. The job stops paying for workers that sit idle, such as during file listing or skewed stages. Learn more: Using Auto Scaling for AWS Glue (opens in a new tab)
Explanation
Non-urgent batch jobs can cut Glue cost in two ways without changing their logic. Flex execution uses spare capacity at a lower price for jobs that can wait for it, and auto scaling keeps only the workers that have work to do. Bigger workers, a Python shell rewrite and a duplicate run all increase cost or risk the deadline.
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