- A
Set up a CloudWatch alarm on the endpoint's invocation latency
Why wrong: Latency does not indicate drift.
- B
Periodically retrain the model using all historical data
Why wrong: Does not detect drift; wasteful.
- C
Use Amazon S3 events to trigger a Lambda function that compares distributions
Why wrong: Requires custom code; not automatic.
- D
Enable Amazon SageMaker Model Monitor to continuously check for data drift
Built-in drift detection.
Question 1,381 of 1,755
How to Automatically Detect Data Drift on SageMaker Endpoints
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'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?
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 Amazon SageMaker Model Monitor to continuously check for data drift
Amazon SageMaker Model Monitor is purpose-built to automatically detect data drift by continuously comparing incoming inference data against a baseline dataset. It computes statistical metrics (e.g., distribution distances like Kolmogorov-Smirnov or Chi-squared) and raises alerts when drift exceeds configurable thresholds, enabling proactive retraining without manual intervention. This directly addresses the need for automated drift detection in a low-latency customer-facing application.
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.
- ✗
Set up a CloudWatch alarm on the endpoint's invocation latency
Why it's wrong here
Latency does not indicate drift.
- ✗
Periodically retrain the model using all historical data
Why it's wrong here
Does not detect drift; wasteful.
- ✗
Use Amazon S3 events to trigger a Lambda function that compares distributions
Why it's wrong here
Requires custom code; not automatic.
- ✓
Enable Amazon SageMaker Model Monitor to continuously check for data drift
Why this is correct
Built-in drift detection.
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 confusing operational metrics (latency, errors) with data quality metrics (drift), leading candidates to choose CloudWatch alarms (Option A) instead of the dedicated monitoring service.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor uses a pre-processing script to capture inference requests and responses from the endpoint, storing them in S3. It then runs a baseline job to compute expected distributions (e.g., mean, variance, quantiles) and a monitoring schedule that periodically compares live data using statistical tests like the Kolmogorov-Smirnov test for continuous features or the Chi-squared test for categorical features. A real-world scenario is a fraud detection model where a sudden shift in transaction amounts (e.g., due to a new payment method) triggers a drift alert, prompting retraining without impacting the customer-facing latency.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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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 Amazon SageMaker Model Monitor to continuously check for data drift — Amazon SageMaker Model Monitor is purpose-built to automatically detect data drift by continuously comparing incoming inference data against a baseline dataset. It computes statistical metrics (e.g., distribution distances like Kolmogorov-Smirnov or Chi-squared) and raises alerts when drift exceeds configurable thresholds, enabling proactive retraining without manual intervention. This directly addresses the need for automated drift detection in a low-latency customer-facing application.
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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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 wants to monitor SageMaker endpoints for data drift. Which TWO services can be used together to detect and alert on drift?
easy- A.SageMaker Data Wrangler
- ✓ B.SageMaker Model Monitor
- C.AWS CodePipeline
- ✓ D.Amazon CloudWatch Alarms
- E.Amazon CloudWatch Logs
Why B: SageMaker Model Monitor (option B) continuously monitors models for data and quality drift. Amazon CloudWatch Alarms (option D) can be set up on Model Monitor's metrics to trigger alerts when drift is detected. SageMaker Data Wrangler (option A) is for data preparation, not monitoring. AWS CodePipeline (option C) is for CI/CD. Amazon CloudWatch Logs (option E) is for log storage and analysis, not for alerting on drift.
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Last reviewed: Jul 4, 2026
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