MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare different hyperparameter configurations and visualize the impact on model accuracy. What should they use to track hyperparameters?
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
✓
SageMaker Experiments
SageMaker Experiments allows you to log hyperparameters as parameters. They can be viewed and compared across runs in the SageMaker Studio UI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger is for monitoring training jobs and detecting issues, not for tracking hyperparameters.
- ✗
SageMaker Autopilot
Why it's wrong here
Autopilot automates model building and tuning, but it is not the tool for manually tracking experiments.
- ✓
SageMaker Experiments
Why this is correct
Experiments track hyperparameters, metrics, and artifacts for comparison.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor is for monitoring deployed models for data drift, not for tracking hyperparameters.
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