Question 1,268 of 1,672
MLS-C01 Modeling Practice Question
A machine learning engineer is using Amazon SageMaker to train a model. The training job fails with an out-of-memory error. The training data size is 10 GB and the instance is ml.m5.xlarge (16 GB memory). Which change is MOST likely to resolve the issue without increasing cost?
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.
Many algorithms allow you to set a batch size, and reducing it lowers memory usage. Option B is wrong because changing to GPU may not help and could increase cost. Option C is wrong because increasing instance type increases cost. Option D is wrong because decreasing the dataset size may lose information.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reduce the batch size in the training script.
Why this is correct
Smaller batch size reduces memory footprint per iteration.
- ✗
Switch to a GPU instance like p3.2xlarge.
Why it's wrong here
GPU instances may have less memory and are more expensive.
- ✗
Use a larger instance type like ml.m5.4xlarge.
Why it's wrong here
Larger instance increases cost.
- ✗
Decrease the training dataset size.
Why it's wrong here
Reducing data may hurt model quality.
Visual reference
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Last reviewed: Jun 20, 2026
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.
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