MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Experiments to track multiple training runs for a PyTorch model. They want to compare metrics across runs and identify the best hyperparameters. Which TWO capabilities should they use? (Choose TWO.)
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 list and search API to query runs by metric
SageMaker Experiments automatically tracks hyperparameters and metrics. The SDK allows logging custom metrics. The Experiments list and search interface can compare runs. Autopilot is for AutoML, not for custom PyTorch. Model Monitor is for deployed models.
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 Experiments list and search API to query runs by metric
Why this is correct
The list and search API allows filtering and comparing runs based on metrics.
- ✓
SageMaker SDK's experiment logging capabilities
Why this is correct
The SDK allows logging hyperparameters and metrics during training for later comparison.
- ✗
SageMaker Autopilot
Why it's wrong here
Autopilot is for automatic ML, not for tracking custom PyTorch runs.
- ✗
SageMaker Clarify
Why it's wrong here
Clarify is for bias detection and explainability, not for experiment tracking.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor is for monitoring deployed models, not for experiment tracking.
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