- A
Set up Amazon SageMaker Model Monitor to track model performance metrics against ground truth labels as they arrive.
Model performance monitoring directly detects concept drift by comparing predictions to actuals.
- B
Use Amazon SageMaker Clarify to monitor feature attribution drift.
Why wrong: Feature attribution drift is a subset of data drift.
- C
Enable Amazon CloudWatch to monitor model endpoint latency.
Why wrong: Latency is about performance, not prediction accuracy.
- D
Configure Amazon SageMaker Model Monitor to track data drift on the input features.
Why wrong: Data drift monitors input distribution, not prediction accuracy.
Question 673 of 1,000
Monitoring Model Performance Degradation Using Ground Truth in SageMaker
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 science team deploys a regression model to Amazon SageMaker for real-time inference. After one month, the model's prediction errors increase significantly, but data distributions remain unchanged. Which monitoring approach is MOST suitable for detecting this issue?
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
✓
Set up Amazon SageMaker Model Monitor to track model performance metrics against ground truth labels as they arrive.
Amazon SageMaker Model Monitor can be configured to track model performance metrics (e.g., regression error metrics like RMSE or MAE) against ground truth labels as they arrive. Since the question states that data distributions remain unchanged but prediction errors increase, the issue is likely model degradation (e.g., concept drift or model staleness) rather than data drift. Monitoring ground truth labels directly captures this performance degradation, making option A the most suitable approach.
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.
- ✓
Set up Amazon SageMaker Model Monitor to track model performance metrics against ground truth labels as they arrive.
Why this is correct
Model performance monitoring directly detects concept drift by comparing predictions to actuals.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon SageMaker Clarify to monitor feature attribution drift.
Why it's wrong here
Feature attribution drift is a subset of data drift.
- ✗
Enable Amazon CloudWatch to monitor model endpoint latency.
Why it's wrong here
Latency is about performance, not prediction accuracy.
- ✗
Configure Amazon SageMaker Model Monitor to track data drift on the input features.
Why it's wrong here
Data drift monitors input distribution, not prediction accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse data drift (changes in input features) with concept drift (changes in the relationship between features and target), and mistakenly choose data drift monitoring (option D) even though the question explicitly states data distributions are unchanged, while the correct approach is to monitor ground truth performance metrics (option A).
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Model Monitor for ground truth capture works by asynchronously ingesting labels from an S3 bucket or Amazon EventBridge, then computing metrics like mean absolute error (MAE) or root mean squared error (RMSE) against predictions. This is distinct from data drift monitoring, which uses statistical tests (e.g., Kolmogorov-Smirnov) on feature distributions. In real-world scenarios, concept drift (where the relationship between features and target changes) can cause prediction errors to spike even when input distributions remain stable, as in a regression model for housing prices that fails after a market shift.
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
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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: Set up Amazon SageMaker Model Monitor to track model performance metrics against ground truth labels as they arrive. — Amazon SageMaker Model Monitor can be configured to track model performance metrics (e.g., regression error metrics like RMSE or MAE) against ground truth labels as they arrive. Since the question states that data distributions remain unchanged but prediction errors increase, the issue is likely model degradation (e.g., concept drift or model staleness) rather than data drift. Monitoring ground truth labels directly captures this performance degradation, making option A the most suitable approach.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data science team deploys a regression model using Amazon SageMaker. After one week, the model's prediction accuracy drops significantly. The team needs to detect this degradation automatically and trigger retraining. Which AWS service should they use to monitor the model's performance over time and set up alerts?
easy- A.AWS CloudWatch
- ✓ B.Amazon SageMaker Model Monitor
- C.Amazon Inspector
- D.AWS Config
Why B: Amazon SageMaker Model Monitor is the correct choice because it is purpose-built to continuously monitor machine learning models deployed on SageMaker endpoints for data drift, feature attribution drift, and prediction quality degradation. It automatically compares live inference data against a baseline, triggers alerts when performance drops, and can be configured to initiate retraining pipelines via AWS Lambda or Step Functions, directly addressing the need to detect accuracy degradation and trigger retraining.
Keep practising
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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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