MLA-C01 ML Model Development Practice Question
A machine learning engineer is using SageMaker Autopilot for AutoML. Which TWO outputs does Autopilot produce?
⚠ Common exam trap
The trap is thinking Autopilot outputs a single model or a tuning job summary; candidates must remember it produces an ensemble and an explainability report as key artifacts.
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
✓
An ensemble of candidate models
SageMaker Autopilot produces an ensemble of candidate models (B), because it automatically explores multiple algorithms and hyperparameter configurations and then combines the best-performing candidates into an ensemble for deployment. It also produces an explainability report (E), which provides feature importance and model insights so users can understand how the model makes predictions. Autopilot does not output a hyperparameter tuning job summary (A); while it performs hyperparameter optimization internally, the deliverable is not a tuning job summary. It does not create a data labeling pipeline (C), since Autopilot assumes labeled tabular data and does not manage annotation workflows. It also does not produce only a single optimal model (D), because its output includes multiple candidates and an ensemble rather than just one model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A hyperparameter tuning job summary
Why it's wrong here
Autopilot produces a model leaderboard and candidate notebooks, not a standalone tuning job summary; its internal hyperparameter search is not surfaced as that artefact. It is tempting because Autopilot does tune hyperparameters, but a tuning job summary belongs to a separate SageMaker tuning job.
- ✓
An ensemble of candidate models
Why this is correct
Autopilot automatically selects and combines the best-performing pipelines into a single ensemble model, which typically outperforms any individual candidate. This satisfies the requirement for a deployable artefact alongside the generated notebooks and leaderboard, rather than just raw training data or a single fixed algorithm.
- ✗
A data labeling pipeline
Why it's wrong here
Autopilot automates model selection, training and tuning; it does not create labelling workflows or human annotation jobs. A data labelling pipeline is what Amazon SageMaker Ground Truth provides when raw unlabelled data needs annotation before training, a separate preprocessing concern from AutoML.
- ✗
A single optimal model
Why it's wrong here
Autopilot returns a ranked leaderboard of candidate pipelines plus the notebooks and generated code, not one winner; selection depends on the objective metric you specify. A single optimal model is what you obtain after manually evaluating those candidates, or from a service that trains and tunes one algorithm only.
- ✓
An explainability report
Why this is correct
Autopilot generates an explainability report alongside the model leaderboard, showing feature importance and model behaviour. This satisfies the AutoML output constraint by documenting how the best candidate model reached its predictions, supporting review and governance before deployment.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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