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MLA-C01 Practice Question: A team wants to track and compare multiple…
A team wants to track and compare multiple machine learning experiments, including hyperparameters, metrics, and artifacts. They are using Amazon SageMaker. Which AWS service or feature should they use to achieve this?
⚠ Common exam trap
Watch out — candidates often confuse SageMaker Studio (the IDE) with SageMaker Experiments (the tracking service), assuming Studio alone provides experiment tracking, but Studio is merely the interface that can visualize experiment data stored by Experiments.
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
✓
Amazon SageMaker Experiments
Amazon SageMaker Experiments is the correct service because it is specifically designed to track and compare machine learning experiments, including hyperparameters, metrics, and artifacts. It provides a structured way to log, organize, and analyze multiple runs, enabling teams to identify the best-performing model configurations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS CloudTrail
Why it's wrong here
CloudTrail logs AWS API activity for auditing and compliance, capturing who called which API and when, not hyperparameters, metrics or artifacts. It is the right choice for security auditing and change tracking, not for comparing machine learning experiment runs.
- ✓
Amazon SageMaker Experiments
Why this is correct
SageMaker Experiments groups runs into experiment entities, automatically capturing hyperparameters, metrics, and artifacts for each trial. This lets the team track and compare multiple training runs side by side, directly satisfying the stated requirement to log and contrast experiments.
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Amazon SageMaker Model Registry
Why it's wrong here
Model Registry catalogues trained model versions and their approval status for deployment governance; it stores no experiment runs, hyperparameters or metrics. It is the right choice when promoting a model through staging to production, not for comparing training experiments.
- ✗
Amazon SageMaker Studio
Why it's wrong here
Studio is the integrated development environment hosting notebooks and the experiments UI, but it does not itself record runs, hyperparameters or artifacts. Studio is the correct choice for authoring and debugging code interactively, not for the tracking and comparison the stem requires.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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