MLS-C01 Practice Question: Machine Learning Implementation and Operations
A team uses SageMaker to train a deep learning model. They notice the training job is using only a fraction of the GPU memory. Which configuration change would most improve GPU utilization?
⚠ Common exam trap
Candidates often confuse memory fragmentation (which is a memory allocation issue) with overall utilization, and incorrectly assume reducing batch size will fix fragmentation when the real problem is underutilization due to insufficient computational load.
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
Increasing the batch size allows each training step to process more data samples simultaneously, which increases the computational load per step and better saturates the GPU's parallel processing units. This directly improves GPU memory utilization because larger batches keep more tensors resident in memory and increase the arithmetic intensity of matrix operations, reducing idle 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
Larger batch sizes consume more GPU memory and improve utilization.
- ✗
Decrease the batch size to reduce memory fragmentation
Why it's wrong here
Smaller batch sizes reduce utilization.
- ✗
Use a single GPU instead of multiple GPUs
Why it's wrong here
Single GPU may have lower throughput.
- ✗
Enable SageMaker Managed Spot Training
Why it's wrong here
Spot training does not affect GPU utilization.
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