MLS-C01 Modeling Practice Question
A team is training a deep learning model using TensorFlow on a single GPU instance in SageMaker. The GPU utilization is below 30%. Which change will MOST improve GPU utilization?
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
Increasing the batch size makes more efficient use of GPU memory and parallel processing, improving utilization.
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 number of epochs
Why it's wrong here
Reducing epochs shortens training but does not improve GPU utilization per step.
- ✓
Increase the batch size
Why this is correct
Larger batches keep the GPU busy with more data per iteration.
- ✗
Use SageMaker Distributed Training with multiple GPUs
Why it's wrong here
Distributed training adds overhead; single GPU utilization may not improve.
- ✗
Switch to a CPU instance
Why it's wrong here
CPU utilization is different; GPU utilization remains low.
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