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.
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Parameter
Why it's wrong here
Parameters hold configuration inputs fixed before a run, such as learning rate or batch size, so they cannot record an F1 score produced during or after training. Metrics are the component intended for numeric evaluation outputs, and logging F1 there enables the cross-run comparison the scenario requires.
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Hyperparameter
Why it's wrong here
Hyperparameters are the input configuration values set before training, such as learning rate or batch size, not the resulting evaluation output. It is tempting because hyperparameters are logged per run and compared across trials, but the F1 score is a metric produced by the run, so it belongs in the metrics component.
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Artifact
Why it's wrong here
Artifacts are the input or output objects of a trial component, such as datasets or model files, not scalar metrics. It is tempting because artifacts capture run outputs, but F1 scores are numeric values that must be logged as metrics so SageMaker Experiments can chart and compare them across runs.
- ✓
Metric
Why this is correct
Metrics are the SageMaker Experiments component that logs numeric values such as F1 score against a run, satisfying the requirement to compare F1 scores across runs. They are recorded via the log_metric call within a run.
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