MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company has a SageMaker endpoint that serves predictions for a mobile app. The endpoint is deployed on a single ml.m5.large instance. Recently, users have reported that the app sometimes returns outdated predictions. The data science team has confirmed that the model is updated daily by retraining with new data and creating a new endpoint configuration. However, the endpoint still returns predictions from the old model for some requests. The team has verified that the new endpoint configuration is associated with the endpoint and that the endpoint is in service. What is the most likely cause of this issue?
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
✓
The endpoint has multiple variants and the old variant still has a weight assigned
When a SageMaker endpoint has multiple variants with assigned weights, traffic is distributed proportionally. If the old variant still has a weight greater than zero, some requests will continue to be served by the old model, causing outdated predictions. Option A is incorrect because SageMaker endpoints do not cache model artifacts; they load the model from S3 into memory. Option C is incorrect because the mobile app's CDN caching is unrelated to the SageMaker endpoint's model selection. Option D is incorrect because the team confirmed that the new endpoint configuration is associated with the endpoint and the endpoint is in service, meaning the configuration is deployed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The old model artifacts are still being cached by the endpoint
Why it's wrong here
Model artifacts are not cached; the endpoint loads the model from S3.
- ✓
The endpoint has multiple variants and the old variant still has a weight assigned
Why this is correct
If the old variant has a weight, it will continue to serve traffic. The new variant should get a weight of 1 and the old variant weight should be set to 0.
- ✗
The mobile app is using a CDN that caches the predictions
Why it's wrong here
The CDN issue is possible but less likely than a variant weight issue.
- ✗
The new endpoint configuration has not been deployed to the endpoint
Why it's wrong here
The team verified that the new configuration is associated and the endpoint is in service.
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 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.