- A
Amazon SageMaker Ground Truth
Why wrong: Ground Truth is for labeling data.
- B
Amazon CloudWatch Logs
Why wrong: CloudWatch can monitor logs but not detect drift.
- C
Amazon SageMaker Clarify
Why wrong: Clarify is for bias and explainability, not drift.
- D
Amazon SageMaker Model Monitor
Model Monitor checks for data and model drift.
Question 1,131 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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 company wants to monitor a deployed model for data drift. Which AWS service 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
✓
Amazon SageMaker Model Monitor
Amazon SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor deployed machine learning models for data drift and quality issues. It automatically detects deviations in the input data distribution compared to a baseline, triggering alerts when drift exceeds defined thresholds, which directly addresses the company's requirement.
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.
- ✗
Amazon SageMaker Ground Truth
Why it's wrong here
Ground Truth is for labeling data.
- ✗
Amazon CloudWatch Logs
Why it's wrong here
CloudWatch can monitor logs but not detect drift.
- ✗
Amazon SageMaker Clarify
Why it's wrong here
Clarify is for bias and explainability, not drift.
- ✓
Amazon SageMaker Model Monitor
Why this is correct
Model Monitor checks for data and model drift.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse SageMaker Clarify (bias/explainability) with Model Monitor (drift/quality), as both involve analyzing model behavior but serve fundamentally different purposes.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor works by capturing inference requests and responses, comparing them against a baseline statistics file generated from the training data using constraints like mean, standard deviation, and quantile boundaries. It uses the `sagemaker-model-monitor-analyzer` container to compute distribution distances (e.g., Jensen-Shannon divergence) and triggers CloudWatch Events when drift is detected. In a real-world scenario, a model trained on customer purchase data from 2022 may experience drift if 2023 buying patterns shift, and Model Monitor would flag this automatically.
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: Amazon SageMaker Model Monitor — Amazon SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor deployed machine learning models for data drift and quality issues. It automatically detects deviations in the input data distribution compared to a baseline, triggering alerts when drift exceeds defined thresholds, which directly addresses the company's requirement.
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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