MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company has deployed a machine learning model on a SageMaker endpoint that serves predictions to a web application. The model uses a custom inference container that loads the model artifacts from an ECR repository. After updating the model with new training data, the data scientist creates a new model and updates the endpoint. However, some users report that they still get predictions from the old model. The data scientist confirms that the endpoint configuration points to the new model. What is the most likely cause?
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
✓
DNS caching on the client side is resolving to the old endpoint IP address
DNS caching on the client side can cause the endpoint's DNS name to resolve to an old IP address, especially if the endpoint's underlying instances have not changed. This explains why some users still receive predictions from the old model even though the endpoint configuration points to the new model. Option A is incorrect because incorrect model artifacts would affect all users uniformly. Option B is incorrect because SageMaker endpoints do not have load balancers in the traditional sense; the endpoint itself routes traffic to the instances. Option C is incorrect because SageMaker automatically handles container image updates when a new model is deployed to an endpoint.
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 new model artifacts are not correctly uploaded to S3
Why it's wrong here
Incorrect. If the new model artifacts were not correctly uploaded to S3, the new model would fail to load for all users, not just some.
- ✗
The endpoint is behind a load balancer that is not updated
Why it's wrong here
Incorrect. SageMaker endpoints do not use external load balancers; the endpoint service itself manages traffic distribution.
- ✗
The inference container is cached and not pulling the new image
Why it's wrong here
Incorrect. SageMaker pulls the inference container image from ECR when the model is created and does not cache it in a way that prevents updates.
- ✓
DNS caching on the client side is resolving to the old endpoint IP address
Why this is correct
Correct. DNS caching at the client side can cause the endpoint's DNS name to resolve to an old IP address, particularly if the endpoint's underlying instance IPs have not changed, leading some users to still hit the old model.
Visual reference
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.