MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning engineer is responsible for deploying a model that was trained using a custom algorithm in Amazon SageMaker. The engineer has built a Docker container that includes the inference code and has tested it locally. The engineer now wants to deploy the container to a SageMaker endpoint for real-time inference. The engineer has already created the model in SageMaker by specifying the image URI and the model artifacts location in S3. However, when the engineer tries to create an endpoint configuration, the operation fails with an error indicating that the model is not in an 'Active' state. What should the engineer do to resolve 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
✓
Check the CloudWatch logs for the container to ensure the inference server starts correctly
When a model is not in 'Active' state after creation, it typically indicates that the container's health check failed. Checking CloudWatch logs for the container helps identify why the inference server is not starting correctly. Option B is incorrect because the issue is with the model's state, not its name. Option C is incorrect because deleting and recreating the model would not resolve the underlying health check problem without fixing the container. Option D is incorrect because using a different image URI would change the container but not address the health check failure if the root cause is in the inference code or configuration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Check the CloudWatch logs for the container to ensure the inference server starts correctly
Why this is correct
The health check requires the container to respond to a ping request. Logs will show if the server failed to start.
- ✗
Create the endpoint configuration with a different model name
Why it's wrong here
The model name is not the issue; the model state is.
- ✗
Delete and re-create the model, then wait for a few minutes
Why it's wrong here
Waiting alone will not change the state if the container is failing health checks.
- ✗
Re-create the model using a different image URI
Why it's wrong here
The issue is not with the image URI but with the container's health check.
Visual reference
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
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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.