- A
Permutation Feature Importance
Why wrong: Permutation importance is global and not used by SageMaker Model Monitor for drift.
- B
SHAP
SageMaker Model Monitor integrates with SHAP for feature attribution drift monitoring.
- C
Partial Dependence Plots
Why wrong: Partial dependence plots show average marginal effects, not per-feature attributions.
- D
LIME
Why wrong: LIME is a local explanation method, but SageMaker Model Monitor uses SHAP.
Question 970 of 1,000
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance, and security. 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 uses SageMaker Model Monitor to track feature attribution drift. Which technique does SageMaker Model Monitor use to compute feature attributions?
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
✓
SHAP
SageMaker Model Monitor uses SHAP (SHapley Additive exPlanations) to compute feature attributions for model explainability and drift detection. SHAP provides a unified measure of feature importance based on cooperative game theory, ensuring consistent and locally accurate attributions across all features.
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.
- ✗
Permutation Feature Importance
Why it's wrong here
Permutation importance is global and not used by SageMaker Model Monitor for drift.
- ✓
SHAP
Why this is correct
SageMaker Model Monitor integrates with SHAP for feature attribution drift monitoring.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Partial Dependence Plots
Why it's wrong here
Partial dependence plots show average marginal effects, not per-feature attributions.
- ✗
LIME
Why it's wrong here
LIME is a local explanation method, but SageMaker Model Monitor uses SHAP.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that SageMaker Model Monitor uses LIME for explainability because LIME is a popular model-agnostic method, but the service is specifically designed around SHAP for its theoretical properties and integration with the Amazon SageMaker Clarify framework.
Trap categories for this question
Command / output trap
Partial dependence plots show average marginal effects, not per-feature attributions.
Detailed technical explanation
How to think about this question
SHAP values are computed using a kernel-based approximation (KernelSHAP) or tree-based methods (TreeSHAP) depending on the model type, and SageMaker Model Monitor leverages the SHAP library to generate baseline and live attribution distributions. Under the hood, the monitor compares the average SHAP value per feature over a baseline dataset against the current dataset using a distance metric (e.g., Jensen-Shannon divergence) to detect drift. In a real-world scenario, if a credit scoring model's 'income' feature attribution shifts significantly, the monitor triggers an alert, allowing the data scientist to investigate data or concept drift.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance, and Security — This question tests ML Solution Monitoring, Maintenance, and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: SHAP — SageMaker Model Monitor uses SHAP (SHapley Additive exPlanations) to compute feature attributions for model explainability and drift detection. SHAP provides a unified measure of feature importance based on cooperative game theory, ensuring consistent and locally accurate attributions across all features.
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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