The answer is to increase the batch size. Low GPU utilization, typically below 30%, signals that the GPU is starved for data because each batch is too small to fully occupy its parallel processing cores, forcing it to idle while waiting for the next batch. By increasing the batch size, you feed more samples per forward and backward pass, raising arithmetic intensity and keeping the GPU busy, which directly cuts training time on SageMaker. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of GPU compute efficiency versus I/O bottlenecks—a common trap is to mistakenly add more instances or switch instance types, but the root cause is underutilization of existing hardware. Remember the memory tip: “Small batch, GPU catch; big batch, GPU match.”
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
GPU utilization is often limited by the batch size due to the GPU's SIMT architecture, which requires a sufficient number of parallel threads to keep cores busy. Increasing the batch size also improves memory bandwidth utilization and can reduce the number of weight updates per epoch, but it must be balanced against GPU memory limits and potential convergence issues. In SageMaker, using a larger batch size with data parallelism (e.g., Horovod or SageMaker's distributed training) can further scale utilization across multiple GPUs.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.