Question 203 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a model, but the training job fails with an 'Out of memory' error. The instance type is ml.p3.2xlarge. Which action should the data scientist take to resolve the issue?
⚠ Common exam trap
Watch out — candidates often confuse storage-related issues (disk space, data loading) with compute memory (GPU RAM), leading them to select Pipe input mode or Spot instances, which do not address the fundamental memory constraint.
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 mini-batch size in the training script.
The 'Out of memory' error on a single ml.p3.2xlarge instance indicates that the GPU memory is insufficient for the current workload. Reducing the mini-batch size directly decreases the memory footprint per training step, allowing the model to fit within the available GPU memory without changing the instance type or incurring additional costs.
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 Pipe input mode.
Why it's wrong here
Pipe mode addresses I/O, not memory.
- ✗
Increase the number of instances.
Why it's wrong here
Increasing instances does not increase per-instance memory.
- ✓
Reduce the mini-batch size in the training script.
Why this is correct
Reducing batch size reduces memory consumption.
- ✗
Use a Spot instance.
Why it's wrong here
Spot instances do not affect memory.
Visual reference
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Last reviewed: Jun 24, 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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