MLA-C01 ML Model Development Practice Question
A data scientist wants to track hyperparameters, metrics, and artifacts for multiple training runs in SageMaker. They need to compare runs and identify the best performing model. Which SageMaker feature should they use?
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 tracking and comparing runs, including hyperparameters, metrics, and artifacts.
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 Model Monitor
Why it's wrong here
Model Monitor is for monitoring inference quality in production, not for training runs.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger is for monitoring and debugging training jobs, not for tracking and comparing runs.
- ✗
SageMaker Autopilot
Why it's wrong here
Autopilot automates model building but doesn't provide the flexible tracking of custom runs.
- ✓
SageMaker Experiments
Why this is correct
Experiments provides experiment management to log parameters, metrics, and artifacts and compare across runs.
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