easyMultiple Select
MLA-C01 Practice Question: Monitor its Amazon SageMaker real-time endpoint…
A company wants to monitor its Amazon SageMaker real-time endpoint for data quality issues. Which TWO actions should the company take?
⚠ Common exam trap
Many candidates confuse SageMaker Debugger (training-time debugging) with SageMaker Model Monitor (post-deployment data quality), leading them to select Option B instead of recognizing that data capture and baseline creation are the two required actions for endpoint monitoring.
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
✓
Create a baseline from the training data to compare against live data.
Option A is correct because SageMaker Model Monitor requires a baseline (statistics and constraints) generated from the training dataset, which is then compared against the live inference data to detect data quality drift. Option E is correct because Model Monitor relies on data capture, which must be enabled on the real-time endpoint to record the request and response payloads into an Amazon S3 location for monitoring. Option B is incorrect because SageMaker Debugger analyzes training jobs for issues like vanishing gradients or overfitting, not live endpoint data quality. Option C is incorrect because a Lambda function for preprocessing requests does not provide data quality monitoring capabilities. Option D is incorrect because S3 bucket notifications on model artifacts only signal object events and do not evaluate inference data quality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a baseline from the training data to compare against live data.
Why this is correct
A baseline computed from training data defines expected feature distributions, enabling SageMaker Model Monitor to detect drift and data quality violations on live traffic. Without this reference, the endpoint has no statistical standard against which to flag anomalies.
- ✗
Use SageMaker Debugger to analyze training jobs.
Why it's wrong here
SageMaker Debugger inspects tensors, gradients and resource usage during training jobs, so it cannot observe live inference traffic on a deployed endpoint. It would be the correct choice for diagnosing training convergence or vanishing gradients, but the scenario requires monitoring incoming request data for drift and quality violations after deployment.
- ✗
Set up an AWS Lambda function to preprocess incoming requests.
Why it's wrong here
A Lambda preprocessor transforms requests before inference; it neither captures endpoint traffic nor compares live inputs against a baseline, so data quality drift stays undetected. It is tempting because Lambda customises payload handling, and it would be right for request validation or feature transformation rather than monitoring distributional quality.
- ✗
Configure Amazon S3 bucket notifications for model artifacts.
Why it's wrong here
S3 bucket notifications report object-level events such as uploads and deletions, not the inference payloads or feature distributions flowing through a live endpoint. They are the right tool for triggering pipelines when model artefacts change, but data quality monitoring requires capturing endpoint request and response data via data capture and a monitoring schedule.
- ✓
Enable data capture on the SageMaker endpoint.
Why this is correct
Enabling data capture logs endpoint request and response payloads to Amazon S3, supplying the live data that Model Monitor analyses for quality violations. Without capture, there is no live dataset to compare against the baseline, so monitoring cannot function.
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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JA
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.