hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data engineer is using AWS Glue to run an ETL…
A data engineer is using AWS Glue to run an ETL job that joins two large datasets and writes the output to S3 for ML training. The job is failing due to out-of-memory errors. Which THREE actions can help resolve this issue? (Select THREE.)
⚠ Common exam trap
The trap here is that candidates might think reducing worker size (Option E) saves costs and helps memory, but it actually reduces available memory per worker, making out-of-memory errors more likely.
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
✓
Filter unnecessary records early in the transformation
Filtering unnecessary records early in the transformation reduces the amount of data that needs to be processed and shuffled, which directly lowers memory pressure. In AWS Glue, applying filters before joins or aggregations minimizes the dataset size in the Spark execution plan, helping to avoid out-of-memory errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Filter unnecessary records early in the transformation
Why this is correct
Reducing data volume early decreases memory usage.
- ✓
Increase the number of DPUs for the Glue job
Why this is correct
More DPUs provide more memory and parallelism.
- ✓
Partition the input data on the join keys
Why this is correct
Partitioning can reduce shuffle and memory pressure.
- ✗
Switch from Spark to Python shell
Why it's wrong here
Python shell has limited memory and is not suitable for large joins.
- ✗
Use a smaller worker type
Why it's wrong here
Smaller workers reduce memory, worsening the problem.
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
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 835 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.