MLS-C01 Modeling Practice Question
A team is using SageMaker to train a custom PyTorch model on a large dataset (10 TB) stored in S3. The training job is repeatedly failing due to 'OutOfMemory' errors on the GPU. The team is using a single ml.p3.8xlarge instance. Which change is most likely to resolve the issue?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that adding more GPUs (Option A) solves per-GPU memory issues, but the OOM error is per-device and requires reducing per-device memory usage, not increasing the number of devices.
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
✓
Reduce the batch size in the training script
The 'OutOfMemory' error on the GPU indicates that the model and its associated data exceed the available GPU memory. Reducing the batch size directly decreases the memory footprint per training step, allowing the model to fit within the GPU's memory limits. This is the most direct and effective fix for GPU OOM errors, as it reduces the amount of data processed simultaneously without changing the instance type or input mode.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the instance type to ml.p3.16xlarge (more GPUs)
Why it's wrong here
More GPUs do not reduce memory per GPU; if the model is too large, it may still OOM on each GPU.
- ✗
Use managed spot training to reduce cost
Why it's wrong here
Spot training does not resolve memory issues.
- ✓
Reduce the batch size in the training script
Why this is correct
Reducing batch size decreases GPU memory usage per step, resolving OOM errors.
- ✗
Switch the input mode from Pipe to File
Why it's wrong here
File mode downloads data to disk, which does not directly affect GPU memory.
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
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JA
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.