- A
Configure SageMaker Model Monitor's model quality monitoring to compare predictions against actual outcomes collected from a week of production traffic
Model quality monitoring tracks metrics like accuracy, precision, recall over time if ground truth labels are available. A significant drop in these metrics would confirm concept drift.
- B
Immediately retrain the model using the most recent month of data and redeploy to the endpoint
Why wrong: Retraining without confirming concept drift may not address the root cause if the drift is due to temporary anomalies. Also, the team needs to first understand why the predictions changed.
- C
Use Amazon SageMaker Clarify to compute SHAP values and understand which features are driving the new predictions
Why wrong: Clarify provides explainability, but it does not directly detect concept drift. It would help understand the model behavior but not confirm whether the relationship has changed.
- D
Investigate data drift by reviewing the Model Monitor feature distribution constraints and comparing recent input data to the baseline
Why wrong: Data drift monitoring examines feature distributions, not the relationship between features and target. Since predictions changed but features might be similar, this would not confirm concept drift.
Quick Answer
The correct first step is to configure SageMaker Model Monitor’s model quality monitoring to compare predictions against actual outcomes collected from a week of production traffic. This directly addresses concept drift, which occurs when the relationship between features and the target changes, because model quality monitoring tracks metrics like accuracy or prediction error against ground truth labels over time. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your ability to distinguish concept drift from data drift—a common trap is confusing feature distribution shifts (data drift) with prediction-target relationship changes (concept drift). Remember, data drift monitors input features, while concept drift requires ground truth to validate prediction accuracy. A useful memory tip: “Concept = Compare with Correct outcomes.”
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 gaming company uses a SageMaker endpoint for real-time player churn prediction. The model is updated weekly. After a recent retraining, the team notices that the endpoint's predicted probabilities for churn have shifted dramatically: the average predicted probability dropped from 0.3 to 0.05. The team suspects concept drift (the relationship between features and target changed) rather than data drift. They have SageMaker Model Monitor set up for data drift and quality metrics, but not for bias or explainability. The team needs to confirm concept drift and take corrective action. Which approach should the team take FIRST?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Configure SageMaker Model Monitor's model quality monitoring to compare predictions against actual outcomes collected from a week of production traffic
To detect concept drift, the team needs to compare the model's predictions against actual observed outcomes (ground truth). SageMaker Model Monitor's quality monitoring can track prediction accuracy over time if ground truth is provided. Option D (set up Model Monitor's model quality monitoring) is the correct first step. Option A (retrain with more recent data) might help but does not confirm drift. Option B (data drift monitoring) checks feature distribution, not concept drift. Option C (use Clarify for SHAP values) is for feature importance, not drift detection.
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.
- ✓
Configure SageMaker Model Monitor's model quality monitoring to compare predictions against actual outcomes collected from a week of production traffic
Why this is correct
Model quality monitoring tracks metrics like accuracy, precision, recall over time if ground truth labels are available. A significant drop in these metrics would confirm concept drift.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Immediately retrain the model using the most recent month of data and redeploy to the endpoint
Why it's wrong here
Retraining without confirming concept drift may not address the root cause if the drift is due to temporary anomalies. Also, the team needs to first understand why the predictions changed.
- ✗
Use Amazon SageMaker Clarify to compute SHAP values and understand which features are driving the new predictions
Why it's wrong here
Clarify provides explainability, but it does not directly detect concept drift. It would help understand the model behavior but not confirm whether the relationship has changed.
- ✗
Investigate data drift by reviewing the Model Monitor feature distribution constraints and comparing recent input data to the baseline
Why it's wrong here
Data drift monitoring examines feature distributions, not the relationship between features and target. Since predictions changed but features might be similar, this would not confirm concept drift.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Similar concept trap
Data drift monitoring examines feature distributions, not the relationship between features and target. Since predictions changed but features might be similar, this would not confirm concept drift.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Configure SageMaker Model Monitor's model quality monitoring to compare predictions against actual outcomes collected from a week of production traffic — To detect concept drift, the team needs to compare the model's predictions against actual observed outcomes (ground truth). SageMaker Model Monitor's quality monitoring can track prediction accuracy over time if ground truth is provided. Option D (set up Model Monitor's model quality monitoring) is the correct first step. Option A (retrain with more recent data) might help but does not confirm drift. Option B (data drift monitoring) checks feature distribution, not concept drift. Option C (use Clarify for SHAP values) is for feature importance, not drift detection.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 23, 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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