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DP-203 Practice Question: Running a Spark job on Azure Databricks that…

A company is running a Spark job on Azure Databricks that processes 500 GB of data daily. The job frequently fails with 'OutOfMemoryError' during shuffles. The cluster uses 10 workers of type Standard_DS3_v2 (14 GB memory each). Which configuration change should you make to improve stability without over-provisioning?

⚠ Common exam trap

Watch out — candidates often assume adding more workers (Option C) is the only way to fix memory errors, but the question tests understanding that partition size, not just cluster size, is the root cause of shuffle OOM errors.

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

Set spark.sql.shuffle.partitions to a higher value, e.g., 500.

The 'OutOfMemoryError' during shuffles indicates that individual partitions are too large for the executor memory. Increasing `spark.sql.shuffle.partitions` to 500 reduces the amount of data per partition, lowering memory pressure during shuffle operations. This directly addresses the error without adding more hardware.

Answer analysis

Option-by-option breakdown

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

  • Set spark.sql.shuffle.partitions to a higher value, e.g., 500.

    Why this is correct

    Reduces data per partition, easing memory.

  • Increase the driver memory to 28 GB.

    Why it's wrong here

    Driver memory not the bottleneck.

  • Increase the number of workers to 20.

    Why it's wrong here

    Increases parallelism but may not address memory per task.

  • Reduce spark.sql.shuffle.partitions to 100.

    Why it's wrong here

    Increases partition size, worsening OOM.

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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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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