MLS-C01 Modeling Practice Question
A company is using SageMaker to train a model, but the training job fails with an out-of-memory error. Which action should the data scientist take to resolve this issue?
⚠ Common exam trap
Test-takers frequently default to scaling up infrastructure (larger instance) instead of optimizing hyperparameters like batch size, which is a more immediate and cost-effective fix for OOM errors in SageMaker.
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
✓
Decrease the batch size
Decreasing the batch size reduces the memory footprint per training step, directly addressing the out-of-memory (OOM) error. In SageMaker, the training instance's GPU or CPU memory is shared between model parameters, activations, and the batch data; a smaller batch size lowers the peak memory usage, allowing the training job to complete without exceeding the instance's memory limit.
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 type for training
Why it's wrong here
Larger instance may help but is not a direct fix.
- ✓
Decrease the batch size
Why this is correct
Smaller batches use less memory.
- ✗
Increase the learning rate
Why it's wrong here
Learning rate does not affect memory.
- ✗
Increase the number of layers
Why it's wrong here
More layers increase memory.
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Written by Johnson Ajibi, MSc IT Security
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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.