- A
SageMaker Model Monitor - Model Quality Monitor
Why wrong: Requires ground truth labels which are not available in real time for fraud detection.
- B
SageMaker Model Monitor - Data Quality Monitor
Correctly monitors statistical and schema drift against a training baseline.
- C
SageMaker Model Monitor - Feature Attribution Drift Monitor
Why wrong: Monitors changes in SHAP feature importance, not data distribution.
- D
SageMaker Model Monitor - Bias Drift Monitor
Why wrong: Monitors bias metrics, not data distribution drift.
Question 384 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 machine learning team deploys a fraud detection model on a SageMaker endpoint. The model's predictions are used in real-time. The team wants to monitor for data drift by comparing incoming data distributions against a baseline created from the training data. Which SageMaker capability should they use?
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
✓
SageMaker Model Monitor - Data Quality Monitor
SageMaker Model Monitor's Data Quality Monitor is specifically designed to detect data drift by comparing the statistical distribution of incoming inference data against a baseline computed from the training dataset. This capability tracks metrics like mean, variance, and quantiles for each feature, alerting when significant deviations occur. For a fraud detection model requiring real-time monitoring of input distributions, this is the correct choice.
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.
- ✗
SageMaker Model Monitor - Model Quality Monitor
Why it's wrong here
Requires ground truth labels which are not available in real time for fraud detection.
- ✓
SageMaker Model Monitor - Data Quality Monitor
Why this is correct
Correctly monitors statistical and schema drift against a training baseline.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker Model Monitor - Feature Attribution Drift Monitor
Why it's wrong here
Monitors changes in SHAP feature importance, not data distribution.
- ✗
SageMaker Model Monitor - Bias Drift Monitor
Why it's wrong here
Monitors bias metrics, not data distribution drift.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'data drift' (input distribution changes) with 'model quality drift' (prediction performance changes), leading them to select Model Quality Monitor instead of Data Quality Monitor.
Detailed technical explanation
How to think about this question
Data Quality Monitor works by scheduling hourly or daily constraint checks against a baseline statistics file (generated from training data using SageMaker Processing or a built-in container) and comparing it to live inference data captured from the endpoint via CloudWatch and S3. It uses statistical tests like the Kolmogorov-Smirnov test for continuous features and Chi-squared test for categorical features to quantify drift. A real-world scenario is a fraud model trained on historical transaction data where seasonal spending patterns cause a temporary shift in feature distributions, triggering an alert that allows the team to retrain before model accuracy degrades.
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: SageMaker Model Monitor - Data Quality Monitor — SageMaker Model Monitor's Data Quality Monitor is specifically designed to detect data drift by comparing the statistical distribution of incoming inference data against a baseline computed from the training dataset. This capability tracks metrics like mean, variance, and quantiles for each feature, alerting when significant deviations occur. For a fraud detection model requiring real-time monitoring of input distributions, this is the correct choice.
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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