MLA-C01 ML Model Development Practice Question

A machine learning engineer is training a SageMaker job with the TensorFlow estimator and wants to automatically capture model training metadata such as loss curves and accuracy for later comparison, without writing any custom code. Which SageMaker feature should they enable?

⚠ Common exam trap

Many candidates confuse SageMaker Debugger with SageMaker Experiments, because both integrate with training jobs but only one automatically logs scalar metrics for run comparison.

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 automatically tracks training parameters, metrics, and artifacts when a training job is launched within an experiment context. It requires no custom code to capture loss and accuracy, and it provides a UI and API to compare runs. Debugger, Model Monitor, and Clarify serve different purposes and do not provide automatic scalar metric logging for experiment comparison.

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 detects data drift and quality issues on deployed endpoints, not during training. It cannot capture training loss curves or accuracy, and it requires a deployed model and baseline. Using it here would not provide the training metadata needed for experiment comparison, making it the wrong tool for this scenario.
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SageMaker Clarify
Why it's wrong here
Clarify is designed for bias detection and explainability, producing feature attribution reports. It does not automatically log training metrics such as loss and accuracy. While it can run as part of a training job, its outputs are bias and explainability artifacts, not the scalar metrics needed for comparing experiment runs.
✓
SageMaker Experiments
Why this is correct
SageMaker Experiments automatically captures input parameters, metrics, and artifacts from training jobs when the estimator is created within an experiment context. It logs scalar metrics like loss and accuracy without custom code, enabling comparison across runs. Enabling it on the TensorFlow estimator satisfies the requirement for automatic metadata capture and later comparison.
✗
SageMaker Debugger
Why it's wrong here
Debugger captures tensors and system metrics for debugging, but it does not automatically log scalar metrics such as loss curves and accuracy for experiment comparison. It requires configuring rules or hooks, and its primary output is debugging data, not experiment metrics. Enabling Debugger alone will not populate the metrics that SageMaker Experiments needs for run comparison.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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