easyMultiple ChoiceObjective-mapped
Monitoring Model Performance Degradation Using Ground Truth in SageMaker
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?
Quick Answer
The detail that data distributions remain unchanged is what rules out a data-drift explanation and points toward the model itself becoming stale relative to a relationship that has shifted over time, sometimes called concept drift, where the same inputs no longer map to the same outputs as reliably as they once did. Detecting that requires comparing what the model predicts against what actually happened, not just watching the incoming feature values, which is why setting up SageMaker Model Monitor to track performance metrics against ground truth labels as they arrive is the right approach: it measures the thing that's actually degrading, prediction error, rather than a proxy like input statistics. A monitor configured only to watch for data drift would show nothing unusual here, since the inputs haven't changed, which is exactly the trap this scenario is testing for; the fix is to monitor outcomes, not just inputs. The general pattern worth remembering is that when a scenario explicitly separates rising errors from unchanged data distributions, it's steering you away from data-drift monitoring and toward ground-truth-based performance monitoring, since that's the only approach that can actually catch a model whose accuracy is declining for reasons unrelated to shifts in incoming data.
⚠ Common exam trap
A common mix-up: candidates 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).
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.
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.
- ✗
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.
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Same concept, more angles
1 more way 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.