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DEA-C01 AWS Glue Worker Types Practice Question

Network Topology
aws glue get-job-runjob-name my-etl-jobrun-id jr_12345output jsonRefer to the exhibit.```"JobRun": {"Id": "jr_12345","JobName": "my-etl-job","JobRunState": "FAILED","StartedOn": "2024-03-15T10:00:00Z","LastModifiedOn": "2024-03-15T11:30:00Z","ExecutionTime": 5400,"DPUSeconds": 108000,"WorkerType": "Standard","NumberOfWorkers": 10,"MaxCapacity": 10

Refer to the exhibit. A data engineer sees this output from the AWS CLI for a failed Glue job. The job uses 10 workers of Standard type. What is the MOST appropriate action to resolve the OutOfMemoryError?

⚠ Common exam trap

Candidates often mistakenly think increasing MaxCapacity raises memory per worker, but when NumberOfWorkers is set, MaxCapacity only sets the maximum total DPU; the job still uses exactly the specified number of workers each with the default DPU for the worker type.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Change WorkerType to G.1X

The OutOfMemoryError occurs because each Standard worker has a memory limit of 4 GB. Increasing MaxCapacity to 20 does not increase memory per worker when NumberOfWorkers is explicitly set; it only increases the total DPU allocation but the job still uses 10 workers each with 1 DPU. The correct solution is to change the worker type to G.1X, which provides 2 DPUs (8 GB memory) per worker, effectively doubling the memory per worker and resolving the error. Option A (increase workers) adds parallelism but does not increase per-worker memory. Option B (reduce workers) decreases parallelism and may aggravate memory pressure. Option D (increase MaxCapacity) is ineffective when NumberOfWorkers is fixed.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Increase NumberOfWorkers to 20

    Why it's wrong here

    Increasing NumberOfWorkers adds more parallelism but does not increase memory per worker; the OutOfMemoryError persists.

  • Reduce NumberOfWorkers to 5

    Why it's wrong here

    Reducing NumberOfWorkers decreases parallelism and reduces total memory, likely making the memory error worse.

  • Change WorkerType to G.1X

    Why this is correct

    Correct. Changing WorkerType to G.1X doubles the memory per worker (from 4 GB to 8 GB), resolving the OutOfMemoryError.

  • Increase MaxCapacity to 20

    Why it's wrong here

    Increasing MaxCapacity to 20 does not increase memory per worker when NumberOfWorkers is set to 10; it only sets a higher total DPU limit but the job still uses 10 workers each with 1 DPU.

Visual reference

Client Recursive Resolver Root DNS (13 root servers) TLD DNS (.com, .org, …) Authoritative example.com query IP addr answer

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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