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?
⚠ Common exam trap
Candidates often confuse shuffle/parallelism tuning knobs (shuffle.partitions, cores) with memory tuning; candidates who see 'slow job' reflexively reach for partition counts instead of recognizing GC as a heap-size symptom.
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.
Garbage collection overhead in Spark is directly tied to the size of the JVM heap on each executor. When executors have too little heap, the JVM runs GC far more frequently and for longer pauses, stalling task execution. Increasing spark.executor.memory enlarges the heap, reducing GC frequency and duration, which is the standard remedy when the Spark UI shows high GC time relative to task time.
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
Excessive garbage collection means each executor's heap is too small for the working set, so the JVM constantly reclaims objects. Raising spark.executor.memory enlarges the heap, reducing GC frequency and duration, which directly addresses the bottleneck the engineer observed in the Spark job.
- ✗
Increase the spark.sql.shuffle.partitions.
Why it's wrong here
Shuffle partitions govern task parallelism, not heap pressure; raising them adds tasks and can worsen GC. It is tempting because skew from too few partitions causes long-running tasks, so tuning this value is right when partitions are too coarse, not when GC dominates.
- ✗
Decrease the number of executor cores.
Why it's wrong here
Fewer cores per executor reduces parallelism without shrinking the per-executor heap, so each task still allocates the same objects and GC pauses persist. Reducing cores helps when excessive concurrent tasks cause memory contention, not when heap sizing is the constraint.
- ✗
Decrease the spark.executor.memoryOverhead.
Why it's wrong here
MemoryOverhead is off-heap memory reserved for JVM overhead and native structures, so reducing it cannot relieve heap pressure causing GC; it risks container kills instead. Raising it is correct when executors are terminated for exceeding container memory limits.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
Related to this question
About these practice questions
This DEA-C01 question is part of Courseiva's 1,321-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.