Question 414 of 1,672
MLS-C01 Modeling Practice Question
A machine learning engineer is using Amazon SageMaker to train a deep learning model. The training job is taking longer than expected. The engineer notices that the GPU utilization is low (around 30%) while CPU utilization is high. Which action is most likely to improve training speed?
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
✓
Increase the number of data loading workers
Low GPU utilization with high CPU utilization suggests a data loading bottleneck. Increasing the number of data loading workers keeps the GPU fed. Reducing batch size or using a smaller instance would not help. Using Pipe mode (streaming) might help but not as directly as increasing workers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the number of data loading workers
Why this is correct
More workers can parallelize data loading and reduce I/O bottleneck, improving GPU utilization.
- ✗
Use a smaller instance type with fewer GPUs
Why it's wrong here
Smaller instance would reduce compute capacity, not solve low GPU utilization.
- ✗
Decrease the number of data loading workers
Why it's wrong here
Decreasing workers would worsen the data loading bottleneck.
- ✗
Increase the batch size
Why it's wrong here
Increasing batch size may increase GPU memory usage but not necessarily improve utilization if data loading is bottleneck.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is training a model using Amazon SageMaker. The training job is running on GPU instances, but the GPU utilization is low. Which TWO actions could improve GPU utilization?
medium- A.Increase the number of epochs
- ✓ B.Use a larger instance with multiple GPUs
- ✓ C.Increase the batch size
- D.Switch to CPU instances
- E.Decrease the batch size
Why B: Using a larger instance with multiple GPUs allows more parallel processing, improving GPU utilization. Option C is correct because increasing the batch size provides more data per step, better utilizing GPU parallelism. Option A is incorrect because increasing epochs does not affect utilization per step. Option D is incorrect because switching to CPU instances would not utilize GPU. Option E is incorrect because decreasing batch size reduces parallelism and lowers GPU utilization.
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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