DEA-C01 Data Operations and Support Practice Question
A data engineer notices that an AWS Glue job processing data from an Amazon S3 bucket frequently fails with 'OutOfMemoryError'. The job reads CSV files, applies transformations, and writes Parquet to another S3 bucket. The job has 10 workers of type G.1X. Which change is MOST likely to resolve the issue?
⚠ Common exam trap
The trap here is that candidates might think adding more workers (Option B) solves memory issues, but OutOfMemoryError is per-worker, not a cluster-wide shortage, so increasing parallelism does not fix the root cause.
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
✓
Change the worker type from G.1X to G.2X
The G.1X worker type provides 16 GB of memory per worker. An OutOfMemoryError indicates that the job's memory requirements exceed this limit. Upgrading to G.2X doubles the memory per worker to 32 GB, directly addressing the memory shortage without changing the parallelism or incurring the overhead of additional workers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Change the worker type from G.1X to G.2X
Why this is correct
G.2X provides 2x the memory of G.1X, directly addressing the OutOfMemoryError.
- ✗
Increase the number of workers to 20
Why it's wrong here
Adding workers distributes the load but does not increase memory per worker; the job still runs out of memory on individual executors.
- ✗
Change the worker type from G.1X to G.8X
Why it's wrong here
G.8X provides 8x memory but is overkill and more expensive; G.2X is sufficient and cost-effective.
- ✗
Enable the Spark UI to monitor memory and tune the job
Why it's wrong here
Monitoring helps diagnose but does not fix the memory shortage; action is still needed.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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