MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare different hyperparameter configurations and visualize the impact on model accuracy. What should they use to track hyperparameters?
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 allows you to log hyperparameters as parameters. They can be viewed and compared across runs in the SageMaker Studio UI.
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 Debugger
Why it's wrong here
Debugger captures tensors and monitors training jobs for anomalies such as vanishing gradients; it does not log hyperparameter configurations for run comparison. It is tempting because it instruments training jobs, and would be correct for diagnosing convergence problems during a run.
- ✗
SageMaker Autopilot
Why it's wrong here
Autopilot automates algorithm selection, tuning and model building end to end; it does not provide a tracking store for comparing manually defined runs. It is tempting because it performs hyperparameter tuning, and would be correct when you want SageMaker to find the best model automatically.
- ✓
SageMaker Experiments
Why this is correct
SageMaker Experiments records each training run as a trial, logging hyperparameters, metrics and artefacts so runs can be compared and charted. It directly satisfies the requirement to track hyperparameter configurations and visualise their effect on accuracy across multiple jobs.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift and quality deviations in deployed endpoints; it does not record hyperparameters or accuracy across training runs. It is tempting because it watches model behaviour over time, and would be correct for alerting on production inference drift.
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