DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to transform data in S3. The Glue job fails with memory errors. Which THREE actions can help resolve this?
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
✓
Optimize the transformation to use pushdown predicates.
Options A, B, and C are correct. Using pushdown predicates (A) reduces the amount of data read by filtering at the data source, which can alleviate memory pressure. Using a larger worker type (B), such as G.2X, increases the memory available per worker, directly addressing out-of-memory errors. Increasing the number of DPUs (C) adds more workers, distributing the memory load. Option D (increasing job timeout) does not solve memory issues, and Option E (decreasing DPUs) would reduce available memory, making the problem worse.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Optimize the transformation to use pushdown predicates.
Why this is correct
Pushdown predicates filter rows in the source system before loading, so fewer records reach the Glue workers, directly easing the memory pressure causing the failures. This reduces the data volume shuffled and held in memory during transformation, satisfying the stem's constraint of resolving out-of-memory errors without simply adding capacity.
- ✓
Use a larger worker type (e.g., G.2X).
Why this is correct
Scaling to a larger worker type such as G.2X increases the memory and CPU allocated to each executor, directly addressing the out-of-memory failures during transformation. Because AWS Glue distributes work across workers, more memory per worker lets each process larger partitions or shuffles without spilling or crashing.
- ✓
Increase the number of DPUs.
Why this is correct
Adding DPUs provisions more Apache Spark executors and memory, distributing the shuffle and aggregation workload so no single executor exhausts heap. This directly addresses the memory errors by raising the job's total processing capacity rather than tuning individual partitions.
- ✗
Increase the job timeout.
Why it's wrong here
Job timeout governs how long a run may execute, not how much memory each executor receives, so an out-of-memory failure still occurs before the timeout matters. Raising the timeout suits long-running jobs that are killed prematurely while still making progress, not memory exhaustion.
- ✗
Decrease the number of DPUs.
Why it's wrong here
Fewer DPUs reduce total executor memory and parallel capacity, worsening the out-of-memory failures rather than relieving them. Scaling DPUs upward is the lever for memory pressure. It is tempting because reducing DPUs lowers cost, which is valid when a job is over-provisioned and running well within its memory budget.
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
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.