Question 219 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model for image classification using TensorFlow on Amazon SageMaker. The model trains slowly, and the GPU utilization is below 20%. Which action will MOST effectively increase GPU utilization and reduce training 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 to better saturate the GPU.
Increasing batch size provides more work per GPU step, improving utilization. Option A is wrong because reducing dataset size does not directly improve GPU utilization and may lead to underfitting. Option C is wrong because switching to CPU would be slower. Option D is wrong because decreasing batch size reduces the work per step, lowering GPU utilization and increasing 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.
- ✗
Reduce the training dataset size.
Why it's wrong here
Reducing dataset size does not directly improve GPU utilization and may lead to underfitting.
- ✓
Increase the batch size to better saturate the GPU.
Why this is correct
Increasing batch size provides more work per GPU step, improving utilization and reducing training time.
- ✗
Switch to a CPU-only instance.
Why it's wrong here
CPU instances are slower for deep learning tasks compared to GPU instances.
- ✗
Decrease the batch size to reduce memory pressure.
Why it's wrong here
Decreasing batch size reduces the amount of work per step, which can lower GPU utilization and increase training time.
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
medium- ✓ A.Increase the batch size
- B.Use a smaller instance type
- C.Increase the learning rate
- D.Reduce the batch size
Why A: 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.
Last reviewed: Jun 20, 2026
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