MLS-C01 Modeling Practice Question
A company is training a large language model on Amazon SageMaker using a single GPU instance. The training is taking too long. Which change would most likely reduce training time?
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 a larger instance with multiple GPUs and enable distributed training
Using multiple GPUs in a distributed training job can parallelize work and reduce time. Option B is wrong because increasing instance memory may not help if the bottleneck is GPU compute. Option C is wrong because decreasing batch size often increases training time due to more updates per epoch. Option D is wrong because S3 Glacier is designed for archival storage with slower retrieval, not faster access.
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 a larger instance with multiple GPUs and enable distributed training
Why this is correct
A larger instance with multiple GPUs and distributed training allows parallel processing of data, reducing training time.
- ✗
Increase the instance memory
Why it's wrong here
Increasing instance memory does not directly speed up training if the bottleneck is compute (GPU).
- ✗
Decrease the batch size
Why it's wrong here
Decreasing batch size generally increases the number of updates per epoch, which can increase training time.
- ✗
Store the training data in S3 Glacier for faster access
Why it's wrong here
S3 Glacier is for long-term archival and has slow retrieval times; it would not speed up data access.
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.