MLS-C01 Practice Question: Machine Learning Implementation and Operations
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 (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.
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.
- ✗
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.
- ✗
Amazon CloudWatch Logs
Why it's wrong here
CloudWatch Logs stores logs but does not detect drift.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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Same concept, more angles
1 more way 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: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.