MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model for image classification using Amazon SageMaker. The training job is taking too long. The data scientist notices that GPU utilization is low (around 30%). Which action is most likely to improve GPU utilization and reduce training time?
⚠ Common exam trap
Candidates often confuse low GPU utilization with a need to reduce batch size (thinking smaller batches speed up training), when in fact increasing batch size is the standard remedy to saturate GPU compute and reduce wall-clock time.
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 (around 30%) indicates that the GPU is spending too much time idle while waiting for data batches to be processed. Increasing the batch size allows each training step to process more samples per forward/backward pass, which increases computational load on the GPU and improves hardware utilization. This directly reduces the number of steps needed per epoch, thereby decreasing overall training time.
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 batch size
Why this is correct
Larger batch size keeps GPU busy, improving utilization and reducing total training time if the data pipeline can keep up.
- ✗
Use a smaller instance type
Why it's wrong here
Smaller instance has less GPU memory and compute, potentially worsening the problem.
- ✗
Increase the learning rate
Why it's wrong here
Learning rate affects convergence speed, not GPU utilization directly.
- ✗
Reduce the batch size
Why it's wrong here
Smaller batch size means less work per GPU step, likely decreasing utilization further.
Go deeper
Related to this question
About these practice questions
One of 1,672 original MLS-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
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.