MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker to train a deep learning model with a large dataset. The training job fails with a 'CUDA out of memory' error. What is the MOST efficient way to resolve this issue?
⚠ Common exam trap
AWS often tests the misconception that 'more resources' (larger instance or more GPUs) is always the best fix, when in fact adjusting hyperparameters like batch size is the most efficient and cost-effective first step.
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
The 'CUDA out of memory' error occurs when the GPU's memory is insufficient to hold the model parameters, gradients, optimizer states, and the current batch of data. Reducing the batch size decreases the memory footprint per training step, allowing the model to fit within the available GPU memory without requiring a more expensive instance or sacrificing GPU acceleration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a CPU-only instance
Why it's wrong here
CPU training is significantly slower for deep learning.
- ✗
Use a larger instance type with more GPUs
Why it's wrong here
More GPUs don't solve per-GPU memory limits unless you use model parallelism.
- ✗
Increase the batch size
Why it's wrong here
Increasing batch size increases memory usage.
- ✓
Reduce the batch size
Why this is correct
Smaller batch size reduces memory consumption per GPU.
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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.