- A
A SageMaker Clarify processing job that computes SHAP values on the captured data
Clarify runs the SHAP analysis on the current data to compare with baseline.
- B
Ground truth labels for the inference data
Why wrong: Ground truth labels are not needed for SHAP-based feature attribution drift.
- C
Baseline constraints file for data quality
Why wrong: Data quality constraints are not required for feature attribution drift.
- D
Real-time endpoint with data capture enabled
Data capture collects inference data for SHAP computation.
- E
A baseline SHAP explainability file from training data
Baseline SHAP values are needed to compare against current attributions.
Question 862 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 for feature attribution drift monitoring with SHAP. Which THREE prerequisites must be in place before starting the monitoring schedule? (Select THREE)
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
✓
A SageMaker Clarify processing job that computes SHAP values on the captured data
Option A is correct because SageMaker Model Monitor requires a Clarify processing job to compute SHAP values on the captured data as part of the feature attribution drift monitoring setup. This job generates the necessary SHAP explainability values that are compared against the baseline to detect drift.
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.
- ✓
A SageMaker Clarify processing job that computes SHAP values on the captured data
Why this is correct
Clarify runs the SHAP analysis on the current data to compare with baseline.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Ground truth labels for the inference data
Why it's wrong here
Ground truth labels are not needed for SHAP-based feature attribution drift.
- ✗
Baseline constraints file for data quality
Why it's wrong here
Data quality constraints are not required for feature attribution drift.
- ✓
Real-time endpoint with data capture enabled
Why this is correct
Data capture collects inference data for SHAP computation.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
A baseline SHAP explainability file from training data
Why this is correct
Baseline SHAP values are needed to compare against current attributions.
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 the prerequisites for feature attribution drift monitoring with those for data quality monitoring, mistakenly selecting the baseline constraints file (Option C) instead of the baseline SHAP explainability file (Option E).
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Model Monitor uses SHAP to compute feature importance scores for each inference request, and these scores are aggregated over time to detect drift. The baseline SHAP explainability file is generated from the training data using SageMaker Clarify, and the monitoring schedule compares the SHAP values from live data against this baseline to identify shifts in feature importance. A real-world scenario is detecting when a model's reliance on a particular feature changes due to data distribution shifts, such as a sudden increase in the importance of a demographic feature after a policy change.
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
Got this wrong? Here's your next step.
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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: A SageMaker Clarify processing job that computes SHAP values on the captured data — Option A is correct because SageMaker Model Monitor requires a Clarify processing job to compute SHAP values on the captured data as part of the feature attribution drift monitoring setup. This job generates the necessary SHAP explainability values that are compared against the baseline to detect drift.
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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