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Data Operations and SupportmediumMultiple SelectObjective-mapped

DEA-C01 Data Operations and Support Practice Question

A data engineer is troubleshooting a Glue ETL job that reads from an S3 bucket and writes to a Redshift table. The job fails with a 'MemoryError' when processing a large dataset. Which TWO actions should the engineer take to resolve this issue? (Choose TWO.)

⚠ Common exam trap

Candidates often confuse 'coalesce(1)' (which reduces parallelism) with a memory-saving technique, or mistakenly think decreasing DPUs or adjusting RedshiftTempDir can fix memory errors, when in fact memory errors require more resources and better partition management.

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 DPUs and set 'spark.sql.shuffle.partitions' to a higher value.

Increasing the number of DPUs (Data Processing Units) provides more memory and compute resources to the Glue job, directly addressing the MemoryError. Setting 'spark.sql.shuffle.partitions' to a higher value reduces the amount of data shuffled per partition, preventing out-of-memory errors during wide transformations like joins or aggregations.

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 number of DPUs and set 'spark.sql.shuffle.partitions' to a higher value.

    Why this is correct

    More DPUs and shuffle partitions distribute data across more executors, reducing per-executor memory load.

  • Increase the number of DPUs and set 'coalesce(1)' in the script.

    Why it's wrong here

    coalesce(1) reduces parallelism, increasing memory pressure on a single executor.

  • Decrease the number of DPUs and increase 'spark.shuffle.partitions'.

    Why it's wrong here

    Decreasing DPUs reduces available memory, worsening the problem.

  • Set the 'RedshiftTempDir' parameter to a larger S3 bucket.

    Why it's wrong here

    RedshiftTempDir is for staging data, not for resolving memory errors in Glue.

  • Set the 'groupFiles' option to 'inPartition' in the S3 source configuration.

    Why this is correct

    groupFiles combines small files into larger partitions, reducing overhead and memory pressure.

Visual reference

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

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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