MLA-C01 ML Model Development Practice Question
A data scientist wants to use SageMaker Autopilot to automatically build a regression model. The dataset contains 200 features and 50,000 rows. Which output does SageMaker Autopilot provide?
⚠ Common exam trap
MLA-C01 often tests the misconception that Autopilot only outputs a single best model, when in fact it provides a leaderboard and explainability reports, so candidates may overlook the comprehensive output.
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
✓
A leaderboard of candidate models with metrics and explainability reports
SageMaker Autopilot automatically explores different algorithms and hyperparameter combinations to find the best model for a given dataset. It provides a leaderboard of candidate models, each with performance metrics, and generates explainability reports that show how features influence predictions. This allows data scientists to understand and select the most suitable model for 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.
- ✗
Only the best model without any metrics
Why it's wrong here
Autopilot always emits candidate metrics and a model leaderboard, so a model without metrics cannot be its output. Training a model while suppressing evaluation metrics suits pipelines where scoring is handled by a separate offline framework.
- ✓
A leaderboard of candidate models with metrics and explainability reports
Why this is correct
SageMaker Autopilot explores preprocessing and algorithms automatically, then returns a leaderboard ranking candidate models by objective metric, alongside notebooks and explainability reports. This satisfies the requirement to automatically build a regression model from 200 features and 50,000 rows.
- ✗
A single optimal model with no further tuning
Why it's wrong here
Autopilot returns a ranked leaderboard of candidate pipelines plus the best model, not one model alone. Selecting a single optimal model with no tuning describes manual training with fixed hyperparameters, which would be the choice when compute budget forbids exploring alternatives.
- ✗
A Python script for manual training
Why it's wrong here
Autopilot generates candidate pipelines, notebooks and a deployable model, not a standalone training script for manual use. A Python script suits teams wanting full control over feature engineering and training code; Autopilot's purpose is automating that model selection and tuning work instead.
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Same concept, more angles
2 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which SageMaker feature automatically generates model cards, feature importance, and bias reports without requiring manual coding?
easy- ✓ A.SageMaker Autopilot
- B.SageMaker Experiments
- C.SageMaker Clarify
- D.SageMaker Model Monitor
Why A: SageMaker Autopilot is an automated machine learning (AutoML) service that automatically explores data, selects algorithms, trains and tunes models, and generates explainability reports including model cards, feature importance, and bias detection—all without writing code. It integrates SageMaker Clarify under the hood to produce these artifacts as part of the AutoML process. The key differentiator is that Autopilot provides these outputs automatically as part of its end-to-end pipeline, whereas other services require manual configuration or coding.
Variation 2. A machine learning engineer is using SageMaker Autopilot for AutoML. Which TWO outputs does Autopilot produce?
medium- A.A hyperparameter tuning job summary
- ✓ B.An ensemble of candidate models
- C.A data labeling pipeline
- D.A single optimal model
- ✓ E.An explainability report
Why B: 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.
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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