MLS-C01 Data Engineering Practice Question
A company uses AWS Glue ETL jobs to transform CSV data from an S3 bucket into Parquet. The jobs often fail with memory errors when processing large datasets. They want to minimize cost and improve reliability. What should they do?
⚠ Common exam trap
Many exam-takers assume increasing the number of workers (Option D) is the universal fix for performance issues, but in Glue, memory errors are typically caused by insufficient per-worker memory, not a lack of parallelism.
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
✓
Use G.1X or G.2X worker types and increase the number of DPUs per worker.
The G.1X and G.2X worker types provide more memory per worker (16 GB and 32 GB, respectively) compared to the standard G.0X worker (4 GB). By using these worker types and increasing the number of DPUs per worker, you allocate more memory to each task, reducing out-of-memory errors when processing large datasets. This approach also optimizes cost by using fewer, more powerful workers instead of many underpowered ones, improving reliability without unnecessary scaling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use G.1X or G.2X worker types and increase the number of DPUs per worker.
Why this is correct
G.1X workers provide more memory and vCPU per worker, reducing OOM errors for memory-intensive transformations.
- ✗
Use Amazon Athena with CTAS queries to convert the data to Parquet.
Why it's wrong here
Athena is interactive and incurs per-query costs; CTAS is good for one-time conversion but not for scheduled ETL workflows.
- ✗
Switch to S3 Batch Operations with AWS Lambda to process the files individually.
Why it's wrong here
S3 Batch Operations is for simple object-level actions, not complex ETL like CSV-to-Parquet conversion.
- ✗
Increase the number of workers in the Glue job configuration.
Why it's wrong here
Adding workers increases parallelism but does not increase memory per worker; memory errors persist if each task requires more memory.
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 MLS-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 MLS-C01 exam.