easyMultiple Select
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 (option C), including data preprocessing, transformations, and generating candidate feature pipelines as part of exploring the dataset and building the best model. It also automatically performs hyperparameter tuning (option E), running many training jobs with different algorithm and hyperparameter combinations to optimize model performance. Autopilot does not handle data ingestion (option A), which is the responsibility of the user to provide data in S3 or another source before starting the job. It also does not perform model deployment (option B), which must be done separately via SageMaker endpoints or batch transform after Autopilot produces the model. Finally, it does not perform data labeling (option D), since Autopilot requires an already-labeled target column for supervised learning.
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 consumes an existing tabular dataset supplied in Amazon S3; it does not ingest or collect source data. It is tempting because ingestion is a genuine pipeline stage, but that belongs to services such as AWS Glue or Kinesis, not to Autopilot's automated model-building workflow.
- ✗
Model deployment
Why it's wrong here
Autopilot produces a trained model and candidate pipeline, but endpoint provisioning and deployment remain manual steps you perform afterwards. It is tempting because Autopilot outputs deployable artefacts, yet automatic deployment belongs to SageMaker Pipelines or hosting configuration, not Autopilot itself.
- ✓
Feature engineering
Why this is correct
SageMaker Autopilot automatically explores transformations such as imputation, encoding, and scaling, generating candidate pipelines without manual intervention. This built-in feature engineering satisfies the question's requirement that Autopilot handle preprocessing as one of its managed aspects.
- ✗
Data labeling
Why it's wrong here
Autopilot requires pre-labelled target data for supervised training; it does not annotate raw records. It is tempting because labelling is a real ML pipeline task, but that belongs to Amazon SageMaker Ground Truth, whereas Autopilot assumes labels already exist in the supplied dataset.
- ✓
Hyperparameter tuning
Why this is correct
SageMaker Autopilot automatically performs hyperparameter tuning as part of its AutoML pipeline, searching parameter combinations to optimise model performance without manual intervention. This satisfies the stem's requirement that Autopilot handle the aspect itself, rather than the data scientist configuring tuning jobs or specifying ranges manually.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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