DEA-C01 Data Operations and Support Practice Question
A data engineer is troubleshooting an AWS Glue ETL job that fails with a 'java.lang.OutOfMemoryError: Java heap space' error. The job processes a 50 GB Parquet file from an S3 bucket. The job uses a G.1X DPU (16 GB memory) and default parameters. Which action should the engineer take 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
✓
Change the worker type to G.2X (32 GB memory).
Changing the worker type to G.2X (32 GB memory) doubles the memory per worker, directly addressing the Java heap space error. Option B is incorrect because increasing the number of workers does not increase memory per worker; each G.1X DPU still has only 16 GB. Option C is incorrect because increasing the batch size would increase the amount of data loaded into memory per worker, potentially worsening the memory issue. Option D is incorrect because converting from Parquet to JSON typically increases file size and memory usage due to lack of compression and columnar storage.
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 to G.2X (32 GB memory).
Why this is correct
Doubling the memory per worker resolves the heap space error without changing the number of workers.
- ✗
Increase the number of workers from 2 to 4.
Why it's wrong here
Adding more workers of the same type does not increase memory per worker; the job may still fail on a single worker.
- ✗
Increase the 'batch size' parameter in the DynamicFrame reader.
Why it's wrong here
Larger batches increase per-worker memory consumption, worsening the problem.
- ✗
Convert the input data from Parquet to JSON format.
Why it's wrong here
JSON is less efficient and typically uses more memory than Parquet, making the issue worse.
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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