MLA-C01 ML Model Development Practice Question
A data science team is using SageMaker Experiments to track hyperparameters and metrics for a model training project. They need to compare multiple trials and identify the best model. Which THREE actions are part of a typical workflow? (Select THREE.)
⚠ Common exam trap
MLA-C01 often tests what SageMaker Experiments does versus what adjacent services do — the trap is assuming Experiments auto-generates evaluation artifacts or auto-deploys models, which are responsibilities of other tools.
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
✓
Log hyperparameters and metrics using the SageMaker SDK
Option D is correct because a SageMaker Experiments workflow begins by creating an experiment (via the SageMaker SDK, e.g., Experiment.create) that serves as the top-level container grouping runs and trials for the project. Option A is correct because during training the team logs hyperparameters and metrics to the experiment using the SageMaker SDK (for example with Run/Tracker and log_parameter/log_metric calls), which is what makes trials comparable. Option C is correct because the SDK provides APIs such as Experiment.list_runs or Trial/TrialComponent lookups and metric retrieval so the team can list trials and compare their metrics to identify the best model. Option B does not belong because SageMaker Experiments does not automatically generate confusion matrices for each trial; any such artifact must be computed and logged explicitly by the training code. Option E does not belong because automatic deployment of the best trial to an endpoint is not part of the Experiments tracking workflow; deployment is a separate step typically handled via the SageMaker model registry, pipelines, or manual deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Log hyperparameters and metrics using the SageMaker SDK
Why this is correct
Logging hyperparameters and metrics through the SageMaker SDK writes each trial's parameters and metric values into the experiment run, satisfying the need to capture comparable data across trials. Without this instrumentation, no run records exist for the SDK's analytics and visualisation tools to rank or compare, so identifying the best model becomes impossible.
- ✗
Generate confusion matrices for each trial automatically
Why it's wrong here
Confusion matrices are produced by model evaluation tooling such as SageMaker Clarify or the training container's logs, not generated automatically by Experiments, which records logged metrics and artefacts. It is tempting because classification quality needs them, and they would be correct as evaluation output for a classification trial.
- ✓
Use the SageMaker SDK to list trials and compare metrics
Why this is correct
Listing trials through the SageMaker SDK and comparing their metrics directly satisfies the requirement to evaluate multiple runs and identify the best model. The SDK's search and analytics APIs retrieve trial components and their metric values, enabling programmatic ranking across experiments without manual inspection of individual training jobs.
- ✓
Create an experiment in SageMaker Experiments
Why this is correct
Creating an experiment establishes the top-level container that groups related trials, runs, and metrics. Subsequent training jobs log their hyperparameters and metrics as trials within it, enabling the team to compare trials and select the best model.
- ✗
Automatically deploy the best trial to an endpoint
Why it's wrong here
Experiments tracks runs, parameters and metrics; deployment is a separate SageMaker Pipelines or endpoint step performed after a trial is selected. It is tempting because the workflow's purpose is choosing a best model, and automatic deployment would be correct in a CI/CD pipeline with approval gates.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.