Quick Answer
The answer is to increase the batch size. This is correct because a small batch size causes the GPU to process data too quickly, leaving compute units idle while waiting for the next batch to be loaded from CPU memory; by increasing the batch size, the GPU can process more data in parallel per training step, keeping its cores fully occupied and reducing idle time. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of GPU utilization bottlenecks in SageMaker TensorFlow training—a common trap is to assume you need a larger instance or more GPUs, but the simplest fix is often tuning the batch size. Remember the memory tip: “Small batches, GPU scratches its head; big batches, GPU is fed.”
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 using TensorFlow on Amazon SageMaker. The training job uses a single GPU instance but the GPU utilization is low. Which action is MOST likely to improve GPU utilization?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Increase the batch size
Larger batch size better utilizes GPU.
Use a smaller instance type
Why wrong: Smaller instance has less GPU.
Add more features
Why wrong: More features may increase compute but not utilization.
Decrease the number of epochs
Why wrong: Fewer epochs don't improve utilization.
Answer choices
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
Increasing the batch size allows the GPU to process more data in parallel per training step, which keeps the GPU compute units busier and reduces idle time. In TensorFlow on SageMaker, a small batch size can cause the GPU to finish computation quickly and then wait for the next batch to be loaded, leading to low utilization. This is the most direct way to improve GPU throughput without changing the instance or model architecture.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 better utilizes GPU.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Use a smaller instance type
Why it's wrong here
Smaller instance has less GPU.
Add more features
Why it's wrong here
More features may increase compute but not utilization.
Decrease the number of epochs
Why it's wrong here
Fewer epochs don't improve utilization.
Common exam traps
The trap here is that candidates confuse low GPU utilization with overfitting or model complexity, leading them to choose options like adding features or reducing epochs, when the real issue is underutilization of parallel compute resources due to insufficient batch size.
Detailed technical explanation
GPU utilization is often limited by the 'memory wall' or data pipeline bottlenecks; increasing batch size improves arithmetic intensity (ratio of compute to memory access), which is critical for Tensor Core utilization on NVIDIA GPUs. However, batch size must stay within GPU memory limits—exceeding it causes out-of-memory errors. In SageMaker, the default data pipeline (e.g., using tf.data) may also need tuning (e.g., prefetching, parallel calls) to keep the GPU fed, but batch size is the primary lever.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the batch size — Increasing the batch size allows the GPU to process more data in parallel per training step, which keeps the GPU compute units busier and reduces idle time. In TensorFlow on SageMaker, a small batch size can cause the GPU to finish computation quickly and then wait for the next batch to be loaded, leading to low utilization. This is the most direct way to improve GPU throughput without changing the instance or model architecture.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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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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