Question 1,603 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model on Amazon SageMaker for image classification. The training is taking a long time and the GPU utilization is consistently below 30%. What should the data scientist do to improve GPU utilization and reduce training time?
⚠ Common exam trap
Many exam-takers confuse 'low GPU utilization' with 'overfitting' or 'model complexity,' leading them to choose early stopping or reducing layers, when the real issue is insufficient data parallelism per batch.
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 batch size.
Low GPU utilization (below 30%) indicates that the GPU is spending most of its time waiting for data to process, often due to small batch sizes that underutilize the GPU's parallel compute capacity. Increasing the batch size allows the GPU to process more samples per forward/backward pass, improving arithmetic intensity and hardware utilization, which directly reduces total training time on SageMaker.
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 early stopping to stop training earlier.
Why it's wrong here
Early stopping reduces iterations but does not improve per-step utilization.
- ✓
Increase the batch size.
Why this is correct
Larger batches use GPU memory more efficiently and increase utilization.
- ✗
Switch to a CPU-only instance.
Why it's wrong here
CPU instances would be slower, not faster, for deep learning.
- ✗
Reduce the number of layers in the model.
Why it's wrong here
Reducing layers decreases model capacity but does not address GPU utilization.
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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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