MLA-C01 ML Model Development Practice Question
An ML engineer is fine-tuning a large language model using LoRA on SageMaker. The training is converging slowly, and GPU utilization is low. The engineer suspects the bottleneck is data loading. Which action should the engineer take to improve GPU utilization?
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 SageMaker Pipe mode to stream data from S3 directly to the training instances
Low GPU utilization during training is often due to a data pipeline bottleneck. Using SageMaker Pipe mode streams data directly from S3, reducing I/O wait times. Increasing batch size may improve utilization but can cause OOM. Using spot instances and saving checkpoints helps with interruptions but not utilization. Reducing model parallelism may help if communication is the bottleneck, but the scenario suggests data loading.
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 batch size to maximize GPU memory usage
Why it's wrong here
Increasing batch size may cause out-of-memory errors and does not address the data loading bottleneck.
- ✗
Enable checkpointing and use spot instances
Why it's wrong here
Checkpointing and spot instances address cost and fault tolerance, not GPU utilization.
- ✓
Use SageMaker Pipe mode to stream data from S3 directly to the training instances
Why this is correct
Pipe mode reduces I/O latency by streaming data, which can improve GPU utilization.
- ✗
Reduce model parallelism to decrease communication overhead
Why it's wrong here
Reducing model parallelism may help if communication is the bottleneck, but the main issue is data loading.
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
One of 835 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.