Question 160 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
A team has deployed a SageMaker endpoint for a sentiment analysis model. The model was trained on text data from social media. After deployment, the team notices that the model's accuracy has dropped significantly after 3 months. Which action should the team take to detect and address this issue?
⚠ Common exam trap
AWS often tests the distinction between monitoring for operational errors (CloudWatch alarms) versus monitoring for model performance degradation (Model Monitor), and candidates mistakenly choose CloudWatch because they associate 'alarms' with any problem, missing that accuracy drop is a data drift issue, not an invocation error.
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
✓
Enable SageMaker Model Monitor to detect data drift and trigger a retraining pipeline.
B is correct because SageMaker Model Monitor is specifically designed to detect data drift (changes in the input data distribution over time) and model drift (degradation in prediction quality). When a sentiment analysis model trained on social media text sees a drop in accuracy after months, it is likely due to shifts in language, slang, or topics. Model Monitor can continuously track the distribution of input features and predictions against a baseline, and when drift is detected, it can automatically trigger a retraining pipeline to update the model, directly addressing the root cause of the accuracy drop.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker A/B testing to compare with a new model.
Why it's wrong here
A/B testing requires a new model, but does not automatically detect drift.
- ✓
Enable SageMaker Model Monitor to detect data drift and trigger a retraining pipeline.
Why this is correct
Model Monitor can detect drift and trigger automated retraining.
- ✗
Re-deploy the model using the same training script.
Why it's wrong here
Re-deploying the same model will not fix accuracy drop.
- ✗
Create a CloudWatch alarm on invocation errors.
Why it's wrong here
Invocation errors are not related to accuracy.
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Last reviewed: Jul 4, 2026
This MLS-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 MLS-C01 exam.
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