MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare the F1 scores across runs. Which component should they use to log the F1 score?
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
✓
Metric
In SageMaker Experiments, metrics are logged using the SageMaker SDK's log_metric method or by reporting through the training job's metric definitions. Hyperparameters are logged separately. Artifacts are for model files or datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Parameter
Why it's wrong here
Parameters are model weights, not evaluation metrics.
- ✗
Hyperparameter
Why it's wrong here
Hyperparameters are input configurations, not output metrics.
- ✗
Artifact
Why it's wrong here
Artifacts are files such as model outputs, not scalar metrics.
- ✓
Metric
Why this is correct
Metrics are used to track performance values like F1.
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