MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning engineer is deploying a model to an Amazon SageMaker endpoint. The model is a PyTorch model that requires a custom inference script. The engineer notices that the endpoint is returning 500 errors after deployment. Which step should the engineer take to debug the issue?
⚠ Common exam trap
It's easy for candidates to confuse CloudWatch Metrics (aggregate data) with CloudWatch Logs (detailed output), and mistakenly choose metrics thinking they will show error details, when in fact only logs contain the actual error messages and stack traces.
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
✓
View the CloudWatch Logs for the endpoint.
When a SageMaker endpoint returns 500 errors, it typically indicates an application-level failure in the inference container, such as an unhandled exception in the custom inference script. CloudWatch Logs capture the stdout and stderr of the container, including Python tracebacks, which directly reveal the root cause. Option D is correct because viewing these logs is the first and most effective step to diagnose the specific error.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Redeploy the endpoint with a different instance type.
Why it's wrong here
Instance type likely not the cause of code errors.
- ✗
Check the CloudWatch metrics for the endpoint.
Why it's wrong here
Metrics show latency, invocations, but not error details.
- ✗
Modify the inference script and update the endpoint.
Why it's wrong here
Need to see error first before modifying.
- ✓
View the CloudWatch Logs for the endpoint.
Why this is correct
Logs contain stack traces and error messages.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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