easyMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data scientist is using SageMaker Autopilot to…
A data scientist is using SageMaker Autopilot to automatically build a model. Which TWO aspects does Autopilot handle? (Choose TWO.)
⚠ Common exam trap
It's easy for candidates to confuse SageMaker Autopilot's automated capabilities with full MLOps automation, mistakenly thinking it handles data ingestion or deployment, when in fact it focuses solely on model building tasks like feature engineering and hyperparameter tuning.
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
✓
Feature engineering
SageMaker Autopilot automatically performs feature engineering, which includes data preprocessing, feature transformation, and selection of the most relevant features to improve model performance. This is a core capability of Autopilot, as it analyzes the dataset and applies techniques like one-hot encoding, scaling, and imputation without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data ingestion
Why it's wrong here
Autopilot requires the user to provide data in S3; it does not ingest data from external sources.
- ✗
Model deployment
Why it's wrong here
Autopilot does not deploy models; it only recommends models.
- ✓
Feature engineering
Why this is correct
Correct: Autopilot automatically explores different feature transformations.
- ✗
Data labeling
Why it's wrong here
Data labeling is handled by SageMaker Ground Truth, not Autopilot.
- ✓
Hyperparameter tuning
Why this is correct
Correct: Autopilot uses Bayesian optimization to tune hyperparameters.
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