MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using AWS Glue to prepare training data. The job reads from an S3 bucket, performs transformations, and writes to another S3 bucket. The job is failing due to insufficient memory. Which solution should the data scientist use to fix this?
⚠ Common exam trap
Many candidates confuse performance optimization techniques (like using columnar formats or job bookmarks) with resource scaling, assuming any 'best practice' will fix a memory error, when the direct solution is to increase compute/memory allocation via DPUs.
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
✓
Increase the number of DPU (Data Processing Units) for the job.
The job is failing due to insufficient memory, which is a resource constraint. Increasing the number of DPU (Data Processing Units) allocates more memory and compute capacity to the AWS Glue job, directly addressing the out-of-memory error. This is the standard approach to scale Glue ETL jobs when they hit memory limits.
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 AWS Glue's job bookmark feature.
Why it's wrong here
Job bookmarks track processed data, not memory.
- ✓
Increase the number of DPU (Data Processing Units) for the job.
Why this is correct
More workers provide more memory.
- ✗
Use Amazon Athena instead of AWS Glue.
Why it's wrong here
Athena is for ad-hoc queries, not ETL jobs.
- ✗
Use a columnar file format like Parquet.
Why it's wrong here
Parquet reduces storage, not memory during processing.
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 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.