- A
Use SageMaker A/B testing to compare with a new model.
Why wrong: A/B testing requires a new model, but does not automatically detect drift.
- B
Enable SageMaker Model Monitor to detect data drift and trigger a retraining pipeline.
Model Monitor can detect drift and trigger automated retraining.
- C
Re-deploy the model using the same training script.
Why wrong: Re-deploying the same model will not fix accuracy drop.
- D
Create a CloudWatch alarm on invocation errors.
Why wrong: Invocation errors are not related to accuracy.
Question 160 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 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?
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.
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.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor works by capturing inference requests and responses, then comparing statistical properties (e.g., mean, variance, or distribution distances like KL divergence) against a baseline dataset created during training. For text models, it can monitor feature embeddings or raw text distributions using built-in or custom constraints. In a real-world scenario, a social media sentiment model might degrade because of a new trending hashtag or slang term (e.g., 'lit' shifting from positive to neutral), which Model Monitor can flag by detecting a significant shift in the word frequency distribution, triggering a retraining job with fresh data.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-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 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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