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Data Operations and SupportmediumMultiple ChoiceObjective-mapped

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 workers would decrease capacity further, likely worsening the error.

  • Increase the job timeout value.

    Why it's wrong here

    Timeout affects how long the job can run, not resource capacity.

  • Disable Spark UI logging.

    Why it's wrong here

    Disabling logging does not affect capacity; it only reduces logs.

  • Increase the number of workers (DPUs) in the Glue job configuration.

    Why this is correct

    Increasing workers adds more computing capacity, resolving the 'Insufficient capacity' error.

Visual reference

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