DEA-C01 Data Ingestion and Transformation Practice Question
A company is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are failing intermittently with 'Out of Memory' errors. The team wants to resolve this issue without increasing costs significantly. Which TWO actions should the team take?
⚠ Common exam trap
Many exam-takers assume increasing the number of workers (Option C) is the only way to fix OOM errors, but this ignores the cost-effective tuning of worker type upgrades. Another trap is selecting a nonexistent 'memory overhead' parameter (Option A) that sounds plausible but is not a real Glue job configuration option.
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' to double memory per worker.
Switching from G.1x to G.2x workers doubles the memory (and vCPU) per worker while keeping the same worker count, which directly addresses OOM conditions in memory-intensive transformations and is a targeted, cost-controlled change rather than scaling out to maximum workers. Option A is not correct because AWS Glue does not provide a 'Spark memory overhead parameter' in the job configuration; memory overhead is not a user-configurable Glue job setting, so this action cannot be taken as described. Option B is not correct because DynamicFrame vs Spark DataFrame is an API choice for schema handling and does not by itself reduce memory pressure enough to fix OOM errors. Option C is not correct because increasing workers to the maximum allowed raises cost significantly and does not fix per-executor memory limits. Option D is not correct because a Python shell job cannot run distributed Spark ETL at the scale needed for S3-to-Redshift transformations and would not resolve Spark executor OOM issues.
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 memory overhead parameter in the Glue job configuration.
Why it's wrong here
Raising the Spark memory overhead parameter allocates more off-heap memory to each executor, preventing the out-of-memory failures during shuffles and large transformations. It resolves the errors with only a modest configuration change rather than a costly worker-count increase.
- ✗
Use DynamicFrame instead of Spark DataFrame for transformations.
Why it's wrong here
DynamicFrame is built on Spark; memory usage is similar.
- ✗
Increase the number of workers to maximum allowed.
Why it's wrong here
More workers increase parallelism and cost but each worker still has limited memory; may not solve OOM if memory per worker is insufficient.
- ✗
Switch from a Spark job to a Python shell job.
Why it's wrong here
Python shell jobs are for lightweight processing; not suitable for large transformations.
- ✓
Change the worker type from 'G.1x' to 'G.2x' to double memory per worker.
Why this is correct
G.2x workers provide double the memory and vCPU of G.1x at a higher hourly rate, so each executor handles larger partitions and shuffles without spilling to disk, eliminating the out-of-memory failures. This directly addresses the memory constraint while scaling cost proportionally rather than adding workers.
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
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.