DEA-C01 Data Operations and Support Practice Question
A company runs an Amazon EMR cluster with Spark jobs that process data from Amazon S3. The data engineer receives an alert that one of the Spark jobs failed with an OutOfMemoryError. The job processes large files and uses the default Spark configurations. Which configuration change is MOST likely to resolve the issue?
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 configuration.
Increasing spark.executor.memory allocates more memory per executor, directly addressing the OutOfMemoryError when processing large files. Option B (increasing executors) does not increase memory per executor, so each executor remains susceptible to OOM. Option C (disabling dynamic resource allocation) would prevent the cluster from adding resources dynamically, potentially worsening the situation. Option D (decreasing cores per executor) reduces parallelism but does not increase memory per executor; the OOM occurs because each executor lacks sufficient memory, not because of too many cores.
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 configuration.
Why this is correct
Increasing executor memory directly addresses the OutOfMemoryError.
- ✗
Increase the number of executors.
Why it's wrong here
More executors spread the load but each executor still has the same memory limit.
- ✗
Disable dynamic resource allocation.
Why it's wrong here
Dynamic allocation helps manage resources; disabling it could worsen the issue.
- ✗
Decrease the number of cores per executor.
Why it's wrong here
This reduces parallelism and may not help with memory.
Visual reference
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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