- A
The ground truth labels are incorrect, causing the drift
Why wrong: SHAP drift is based on model predictions vs. baseline, not ground truth.
- B
Data drift has occurred for that feature
Why wrong: SHAP drift is about attribution, not the feature distribution itself.
- C
The model is learning equally from all features, so no action is needed
Why wrong: A significant change in SHAP values indicates a shift in feature importance, not equal learning.
- D
The model's behavior has changed, which may lead to future performance degradation and warrants investigation
Feature attribution drift often precedes concept drift; it should be investigated.
Question 358 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 company uses SageMaker Model Monitor to track feature attribution drift with SHAP. They notice that the SHAP values have changed significantly for a feature, while the model performance remains stable. What is the MOST likely interpretation?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
The model's behavior has changed, which may lead to future performance degradation and warrants investigation
Option D is correct because a significant change in SHAP values indicates that the model's internal feature importance has shifted, even if overall performance metrics like accuracy or loss remain stable. This is a classic sign of concept drift or model behavior drift, where the model's decision boundary has changed for that feature, which can lead to future performance degradation as the drift accumulates. SageMaker Model Monitor tracks feature attribution drift separately from data drift, and a change in SHAP values without data drift suggests the model is relying on the feature differently, warranting investigation.
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.
- ✗
The ground truth labels are incorrect, causing the drift
Why it's wrong here
SHAP drift is based on model predictions vs. baseline, not ground truth.
- ✗
Data drift has occurred for that feature
Why it's wrong here
SHAP drift is about attribution, not the feature distribution itself.
- ✗
The model is learning equally from all features, so no action is needed
Why it's wrong here
A significant change in SHAP values indicates a shift in feature importance, not equal learning.
- ✓
The model's behavior has changed, which may lead to future performance degradation and warrants investigation
Why this is correct
Feature attribution drift often precedes concept drift; it should be investigated.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
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 confuse feature attribution drift (SHAP drift) with data drift, assuming a change in SHAP values must be caused by a change in the input data distribution, when in fact it indicates a change in the model's learned behavior that can occur independently of data drift.
Detailed technical explanation
How to think about this question
SHAP values are based on Shapley values from cooperative game theory, measuring each feature's marginal contribution to the model's prediction. When SHAP values drift for a feature, it means the model's internal weighting or interaction with that feature has changed, which can occur due to changes in the underlying relationships in the data (concept drift) even if the input data distribution remains unchanged. In practice, this is often seen in production models where a feature that was previously important becomes less so, or vice versa, due to shifts in customer behavior or external factors, and monitoring SHAP drift is critical for proactive model maintenance.
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?
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: The model's behavior has changed, which may lead to future performance degradation and warrants investigation — Option D is correct because a significant change in SHAP values indicates that the model's internal feature importance has shifted, even if overall performance metrics like accuracy or loss remain stable. This is a classic sign of concept drift or model behavior drift, where the model's decision boundary has changed for that feature, which can lead to future performance degradation as the drift accumulates. SageMaker Model Monitor tracks feature attribution drift separately from data drift, and a change in SHAP values without data drift suggests the model is relying on the feature differently, warranting investigation.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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