DEA-C01 Data Operations and Support Practice Question
A data pipeline using AWS Glue jobs is failing with 'Insufficient capacity' errors for Spark executors. Which action should the data engineer take to resolve this?
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
✓
Increase the number of workers (DPUs) in the Glue job configuration.
The 'Insufficient capacity' error for Spark executors indicates that the Glue job is running out of resources (DPUs). Increasing the number of workers (DPUs) provides more compute capacity, allowing the job to allocate sufficient executors. Option A (reducing workers) would worsen the issue. Option B (increasing timeout) does not add resources. Option C (disabling Spark UI) does not affect capacity. Therefore, increasing the number of workers (DPUs) is the correct resolution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of workers in the Glue job configuration.
Why it's wrong here
Reducing worker count lowers the total executor slots available, worsening the capacity shortfall rather than resolving it. It is tempting because fewer workers cut cost, and that would be correct when a job is over-provisioned and idle, not when Glue cannot obtain enough executors to run the job.
- ✗
Increase the job timeout value.
Why it's wrong here
Timeout values only bound how long a job may run; they cannot create the Spark executor capacity that the service failed to provision. Raising timeouts is tempting when jobs fail partway, but insufficient-capacity errors call for retry configuration, worker-type or worker-count changes, or reduced concurrent demand.
- ✗
Disable Spark UI logging.
Why it's wrong here
Disabling Spark UI logging removes diagnostic telemetry but does not allocate additional Spark executor capacity, so the 'Insufficient capacity' error persists. It is tempting because Spark UI logging consumes driver and worker resources, and disabling it would be the right move when troubleshooting overhead or storage costs, not capacity shortfalls.
- ✓
Increase the number of workers (DPUs) in the Glue job configuration.
Why this is correct
Increasing worker count directly addresses the 'Insufficient capacity' error, which occurs when Glue cannot provision enough DPUs to meet the job's requested executor count. Adding workers raises the total DPU allocation, allowing more Spark executors to launch and satisfy the pipeline's parallelism requirement.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.