MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using Amazon SageMaker to train a model. The training job is taking longer than expected. The data scientist notices that the GPU utilization is low. Which action would most likely improve GPU utilization?
⚠ Common exam trap
A common mix-up: candidates assume low GPU utilization means the GPU is underpowered, leading them to choose a larger instance (Option D), when in fact the issue is a data bottleneck that can be mitigated by increasing batch size.
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 during training often indicates that the GPU is waiting for data to process, a condition known as data bottleneck. Increasing the batch size allows the GPU to process more samples per forward/backward pass, keeping it busier and improving utilization. In SageMaker, this directly impacts the training loop by reducing the frequency of data loading and model update steps.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change to a CPU-based instance
Why it's wrong here
CPU is slower for deep learning.
- ✓
Increase the batch size
Why this is correct
Larger batch sizes keep GPU busy.
- ✗
Decrease the batch size
Why it's wrong here
Smaller batch reduces utilization.
- ✗
Use a larger instance type
Why it's wrong here
This may increase utilization but at higher cost.
- ✗
Enable data augmentation
Why it's wrong here
Data augmentation does not directly impact GPU utilization.
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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.