The answer is to open the SageMaker Autopilot job details and view the 'Explainability' tab. This is correct because SageMaker Autopilot automatically computes SHAP (SHapley Additive exPlanations) values for the best candidate model, presenting feature importance directly in the Explainability tab without any additional configuration or re-running the model. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of Autopilot’s built-in interpretability features—a common trap is assuming you need to manually enable SHAP or run a separate explainability job, when in fact Autopilot handles this automatically. Remember that Autopilot’s Explainability tab is the single-click source for global feature importance, leveraging SHAP to show which features most influence binary classification predictions. Memory tip: “Auto-pilot auto-explains”—the Explainability tab is always ready for the best candidate model.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 company is using SageMaker Autopilot to automatically build a binary classification model. After the AutoML job completes, the data scientist wants to understand which features are most important for the best candidate model. How can the scientist get feature importance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Open the SageMaker Autopilot job details and view the 'Explainability' tab
SageMaker Autopilot automatically generates a 'Explainability' tab within the job details for the best candidate model. This tab uses SHAP (SHapley Additive exPlanations) values to provide feature importance, showing which features most influence the model's predictions. The data scientist can directly access this information without any additional configuration or re-running the model.
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.
✓
Open the SageMaker Autopilot job details and view the 'Explainability' tab
Why this is correct
Autopilot provides feature importance in the explainability tab for the best candidate.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Re-run the best model using SageMaker built-in XGBoost with the 'feature_importance' hyperparameter
Why it's wrong here
Autopilot already includes feature importance; no need to re-run.
✗
Check the CloudWatch Logs for the training job
Why it's wrong here
CloudWatch logs do not contain a summary of feature importance.
✗
Use SageMaker Ground Truth to label a new dataset
Why it's wrong here
Ground Truth is for data labeling, not feature importance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that feature importance must be manually extracted via code or logs, when in fact SageMaker Autopilot provides it directly in the UI under the 'Explainability' tab for the best candidate model.
Detailed technical explanation
How to think about this question
SageMaker Autopilot uses SHAP values for model explainability, which are based on cooperative game theory and provide consistent, locally accurate feature attributions. The 'Explainability' tab displays both global feature importance (across all predictions) and local importance for individual predictions, allowing the scientist to validate model behavior. In a real-world scenario, this helps identify potential data leakage or irrelevant features before deploying the model to production.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Open the SageMaker Autopilot job details and view the 'Explainability' tab — SageMaker Autopilot automatically generates a 'Explainability' tab within the job details for the best candidate model. This tab uses SHAP (SHapley Additive exPlanations) values to provide feature importance, showing which features most influence the model's predictions. The data scientist can directly access this information without any additional configuration or re-running the model.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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This MLS-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 MLS-C01 exam.
Question Discussion
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