MLA-C01 ML Model Development Practice Question
A team is training a PyTorch model using SageMaker with a custom training script. They want to track hyperparameters and metrics across multiple experiments. Which service 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 is the native service for tracking machine learning experiments, including hyperparameters and metrics. SageMaker Debugger is for debugging training jobs. SageMaker Model Monitor is for inference monitoring. SageMaker Clarify is for bias analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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SageMaker Clarify
Why it's wrong here
Clarify is for bias detection and explainability.
- ✓
SageMaker Experiments
Why this is correct
Experiments is designed to track and compare machine learning runs.
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SageMaker Model Monitor
Why it's wrong here
Model Monitor is for monitoring inference endpoints.
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SageMaker Debugger
Why it's wrong here
Debugger is for monitoring training progress and detecting issues, not for experiment tracking.
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