- A
Data ingestion
Why wrong: Autopilot requires the user to provide data in S3; it does not ingest data from external sources.
- B
Model deployment
Why wrong: Autopilot does not deploy models; it only recommends models.
- C
Feature engineering
Correct: Autopilot automatically explores different feature transformations.
- D
Data labeling
Why wrong: Data labeling is handled by SageMaker Ground Truth, not Autopilot.
- E
Hyperparameter tuning
Correct: Autopilot uses Bayesian optimization to tune hyperparameters.
Question 815 of 1,000
easyMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data scientist is using SageMaker Autopilot to…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is using SageMaker Autopilot to automatically build a model. Which TWO aspects does Autopilot handle? (Choose TWO.)
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
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
SageMaker Autopilot uses a multi-step pipeline that includes automatic candidate generation, where it explores different feature engineering transformations (e.g., polynomial features, binning, and text vectorization) and hyperparameter configurations. It leverages Bayesian optimization and random search to tune hyperparameters across multiple trials, selecting the best model based on a user-defined objective metric. In practice, Autopilot can handle up to 250 trials per job and automatically stops if it detects no further improvement, saving compute costs.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Feature engineering — Option C is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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