The answer is to check the CloudWatch logs for the container to ensure the inference server starts correctly. This is the correct action because SageMaker requires a custom container to pass a health check—typically by responding to a GET /ping request on port 8080—before the model status changes from "Pending" to "Active." If the inference server fails to start or the health endpoint returns a non-200 status, the model remains inactive, blocking endpoint creation. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of the custom container health check debugging workflow, a common pitfall where candidates mistakenly focus on model creation or artifact paths instead of container runtime logs. A frequent trap is assuming a locally tested container will automatically pass SageMaker’s health probe, but differences in environment or port binding often cause failures. Memory tip: "Ping to be Active"—your container must respond to the /ping endpoint to become deployable.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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 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
Option C is correct because the model must be in an 'Active' state before it can be deployed, and this requires the container to pass SageMaker's health check. The engineer should check the CloudWatch logs for the container to diagnose the health check failure. Option A is wrong because re-creating the model with the same image will not fix the health check issue. Option B is wrong because the model is already created; the issue is the state. Option D is wrong because the endpoint configuration cannot be created if the model is not active.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Check the CloudWatch logs for the container to ensure the inference server starts correctly — Option C is correct because the model must be in an 'Active' state before it can be deployed, and this requires the container to pass SageMaker's health check. The engineer should check the CloudWatch logs for the container to diagnose the health check failure. Option A is wrong because re-creating the model with the same image will not fix the health check issue. Option B is wrong because the model is already created; the issue is the state. Option D is wrong because the endpoint configuration cannot be created if the model is not active.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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