hardMultiple Select
MLA-C01 Practice Question: Running a SageMaker endpoint serving multiple…
A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality. Which THREE actions are necessary? (Choose three.)
⚠ Common exam trap
Candidates often confuse SageMaker Debugger (for training) with SageMaker Model Monitor (for inference), leading them to select Debugger instead of the correct monitoring schedule and baseline configuration.
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 data capture on the endpoint
Option B is correct because SageMaker Model Monitor requires data capture to be enabled on the endpoint (via DataCaptureConfig) so that inference requests and responses are logged to Amazon S3 for drift and quality analysis. Option D is correct because a Model Monitor schedule (created with CreateMonitoringSchedule) is what actually runs the monitoring jobs on a recurring basis against the captured data. Option E is correct because Model Monitor compares live data to a baseline; constraints and statistics generated from the training dataset (via DefaultModelMonitor.suggest_baseline) define the expected schema and thresholds used to detect drift and quality violations. Option A is not required, since a shadow endpoint is for testing a new model variant, not for monitoring drift or quality of the existing endpoint. Option C is not required, because SageMaker Debugger is used for debugging training jobs (tensors, metrics, profiling), not for production data drift or model quality monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy a shadow endpoint for comparison
Why it's wrong here
A shadow endpoint duplicates live traffic to a candidate model for comparison, which does not measure drift or quality on the production model itself. It is tempting because it compares model behaviour, but that suits pre-deployment validation, whereas drift detection needs Model Monitor baselines on the serving endpoint.
- ✓
Enable data capture on the endpoint
Why this is correct
Data capture records endpoint request and response payloads to Amazon S3, supplying the actual inference data that Model Monitor analyses. Without captured data there is nothing to compare against baselines, so this is a prerequisite for detecting data drift and measuring model quality.
- ✗
Use SageMaker Debugger for monitoring
Why it's wrong here
SageMaker Debugger captures training tensors and metrics, not production inference traffic, so it cannot detect drift or quality decay on a live endpoint. It is tempting because it monitors models, but that is during training jobs; inference monitoring requires Model Monitor with a baseline and schedule.
- ✓
Create a SageMaker Model Monitor schedule
Why this is correct
A Model Monitor schedule runs recurring monitoring jobs against captured endpoint data, comparing it with a baseline and emitting violations to CloudWatch. It provides the automated, ongoing evaluation needed to detect data drift and model quality degradation for the multi-model endpoint.
- ✓
Configure baseline constraints from training data
Why this is correct
Baseline constraints and statistics, generated from training data, define expected feature distributions and thresholds. Model Monitor compares live captured data against these baselines, so drift and quality violations can be detected objectively rather than by arbitrary inspection.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.