MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a deep learning model for image classification. The training job is taking longer than expected. The data scientist observes that GPU utilization is low (around 30%) and CPU utilization is high. Which action is most likely to reduce training time?
⚠ Common exam trap
Many candidates confuse low GPU utilization with a learning rate or batch size issue, when in fact the root cause is a data pipeline bottleneck that requires parallel data loading workers.
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 number of data loading workers
Low GPU utilization with high CPU utilization indicates a data loading bottleneck where the CPU cannot prepare batches fast enough to keep the GPU busy. Increasing the number of data loading workers (e.g., SageMaker's `sagemaker.session.Session` or PyTorch `DataLoader` `num_workers`) allows parallel data preprocessing and I/O, reducing idle GPU time and overall training duration.
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 batch size
Why it's wrong here
Smaller batch sizes may lead to more frequent weight updates but can further underutilize GPU.
- ✗
Increase the batch size
Why it's wrong here
Larger batch sizes may increase memory usage and not improve data loading speed.
- ✗
Increase the learning rate
Why it's wrong here
Learning rate affects convergence speed, not data loading or GPU utilization.
- ✓
Increase the number of data loading workers
Why this is correct
More data loading workers can parallelize data preprocessing and reduce I/O bottleneck, improving GPU utilization.
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