MLS-C01 Modeling Practice Question
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?
⚠ Common exam trap
Watch out — candidates often 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.
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 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.
Answer analysis
Option-by-option breakdown
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.
- ✗
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.
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