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Data Operations and SupportmediumMultiple ChoiceObjective-mapped

DEA-C01 Data Operations and Support Practice Question

A data engineer is running a Spark job on Amazon EMR. The job reads from S3, processes data, and writes to S3. The job is taking longer than expected. The engineer notices that the job is spending a lot of time in the 'GC' (garbage collection) phase. Which configuration change is most likely to improve performance?

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 spark.executor.memory setting.

Increasing executor memory reduces GC frequency. Option B is wrong because it reduces parallelism. Option C is wrong because it reduces memory per task. Option D is wrong because it reduces memory and may increase GC.

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 the spark.executor.memory setting.

    Why this is correct

    More memory reduces GC overhead.

  • Increase the spark.sql.shuffle.partitions.

    Why it's wrong here

    More partitions increase overhead.

  • Decrease the number of executor cores.

    Why it's wrong here

    Reduces parallelism.

  • Decrease the spark.executor.memoryOverhead.

    Why it's wrong here

    Reduces memory for non-heap, likely increases GC.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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