MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to train a large natural language processing model. The training job uses a GPU instance and is expected to take several hours. The data scientist wants to monitor GPU utilization in real-time. Which approach is MOST effective?
⚠ Common exam trap
Test-takers frequently confuse SageMaker Debugger’s ability to capture tensors (which are model-internal data) with hardware monitoring metrics, leading them to choose C, when in fact CloudWatch is the correct service for infrastructure-level monitoring.
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
✓
Enable CloudWatch metrics for the training job and view GPU utilization in the CloudWatch console
Amazon SageMaker automatically publishes GPU utilization metrics (e.g., `GPUUtilization`, `GPUMemoryUtilization`) to Amazon CloudWatch for training jobs running on GPU instances. By enabling CloudWatch metrics (which is the default behavior for SageMaker training jobs), the data scientist can view real-time GPU utilization directly in the CloudWatch console without any code modifications. This is the most effective approach because it requires no changes to the training script and provides native, real-time monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Managed Spot Training to reduce cost and monitor utilization via spot instance status
Why it's wrong here
Spot status does not provide GPU utilization.
- ✗
Modify the training script to periodically log GPU utilization to a file in S3
Why it's wrong here
Modifying the training script to log GPU utilisation to S3 introduces write latency and polling overhead that prevents true real-time monitoring; SageMaker’s native CloudWatch GPU metrics stream directly from the instance’s hardware counters with sub-minute granularity, whereas S3 object writes incur seconds of delay and require manual refresh. This approach is tempting because it gives a custom, persistent audit trail of utilisation over the full training run, and would be correct for post-hoc analysis or compliance logging where real-time visibility is not required.
- ✗
Use SageMaker Debugger to capture GPU utilization tensors
Why it's wrong here
Debugger is for tensors and gradients, not real-time metrics.
- ✓
Enable CloudWatch metrics for the training job and view GPU utilization in the CloudWatch console
Why this is correct
SageMaker automatically publishes GPU metrics to CloudWatch.
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