MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is training a deep learning model on a large dataset using SageMaker. The training job is taking too long. Upon reviewing the CloudWatch logs, the scientist notices that the GPU utilization is below 10% most of the time. Which change is MOST likely to improve GPU utilization and reduce training time?
⚠ Common exam trap
The trap here is that candidates mistakenly think reducing instance size or dataset size will speed up training, when in fact the core issue is underutilization of the existing GPU due to insufficient work per step.
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 in the training script.
Low GPU utilization (below 10%) indicates that the GPU is idle most of the time, waiting for data to be fed. Increasing the batch size allows each training step to process more samples per forward/backward pass, keeping the GPU busy with larger matrix operations and reducing the relative overhead of data loading and kernel launches. This directly improves GPU throughput and reduces total 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.
- ✓
Increase the batch size in the training script.
Why this is correct
Increasing batch size can improve GPU utilization by processing more data per step.
- ✗
Use a different optimizer that requires less computation.
Why it's wrong here
Optimizer choice has minimal impact on GPU utilization compared to data throughput.
- ✗
Switch to a smaller instance type to reduce data transfer overhead.
Why it's wrong here
A smaller instance may have less GPU memory and could worsen the bottleneck.
- ✗
Reduce the size of the training dataset.
Why it's wrong here
Reducing dataset size is not a practical solution and does not address the utilization issue.
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