- A
SageMaker Data Wrangler
Why wrong: Data Wrangler is for data preparation, not monitoring.
- B
SageMaker Model Monitor
Model Monitor detects drift in real-time.
- C
AWS CodePipeline
Why wrong: CodePipeline is for CI/CD, not drift detection.
- D
Amazon CloudWatch Alarms
Alarms can trigger notifications based on Model Monitor metrics.
- E
Amazon CloudWatch Logs
Why wrong: CloudWatch Logs stores logs but does not detect drift.
Quick Answer
The correct answer is Amazon CloudWatch Alarms, used together with SageMaker Model Monitor to detect and alert on data drift. SageMaker Model Monitor continuously analyzes incoming inference data against a baseline to identify statistical shifts, such as changes in feature distributions or prediction quality, which is the core concept of data drift detection. When Model Monitor flags a violation, it publishes metrics to Amazon CloudWatch, where CloudWatch Alarms can be configured to trigger automated notifications or actions, such as retraining pipelines or pausing the endpoint. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this pairing tests your understanding of the monitoring and alerting lifecycle for production ML systems, often appearing in questions that distinguish between services like AWS Glue (for data preparation) or CloudWatch Logs (for log analysis). A common trap is confusing CloudWatch Logs with CloudWatch Alarms—remember that alarms handle threshold-based alerts, not log storage. Memory tip: “Model Monitor measures, CloudWatch alarms.”
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 SageMaker endpoints for data drift. Which TWO services can be used together to detect and alert on drift?
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
SageMaker Model Monitor
SageMaker Model Monitor detects drift and CloudWatch Alarms can send alerts. Options A and D are correct. Option B is for code, Option C is for logs, Option E is for data preparation.
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.
- ✗
SageMaker Data Wrangler
Why it's wrong here
Data Wrangler is for data preparation, not monitoring.
- ✓
SageMaker Model Monitor
Why this is correct
Model Monitor detects drift in real-time.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS CodePipeline
Why it's wrong here
CodePipeline is for CI/CD, not drift detection.
- ✓
Amazon CloudWatch Alarms
Why this is correct
Alarms can trigger notifications based on Model Monitor metrics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon CloudWatch Logs
Why it's wrong here
CloudWatch Logs stores logs but does not detect drift.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Machine Learning Implementation and Operations — study guide chapter
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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: SageMaker Model Monitor — SageMaker Model Monitor detects drift and CloudWatch Alarms can send alerts. Options A and D are correct. Option B is for code, Option C is for logs, Option E is for data preparation.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 MLS-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 company's ML model is deployed on a SageMaker endpoint. The model's predictions are used in a customer-facing application that requires low latency. Over time, the model's performance degrades due to data drift. What is the most suitable approach to detect this drift automatically?
medium- A.Set up a CloudWatch alarm on the endpoint's invocation latency
- B.Periodically retrain the model using all historical data
- C.Use Amazon S3 events to trigger a Lambda function that compares distributions
- ✓ D.Enable Amazon SageMaker Model Monitor to continuously check for data drift
Why D: SageMaker Model Monitor can detect data drift automatically. Option A is wrong because CloudWatch alarms are for infrastructure metrics, not drift. Option B is wrong because S3 events trigger on object changes, not drift. Option D is wrong because retraining on all data is inefficient.
Last reviewed: Jun 20, 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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