AI-300 ML Model Lifecycle And Operations Practice Question
You are automating model registration using the Azure ML CLI. You need to ensure the registration only happens if the model accuracy is above 0.9. How do you implement this condition?
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
✓
Implement logic in the pipeline to gate the registration step.
You must include logic in your pipeline or script to evaluate the metric (e.g., via a 'PythonScriptStep') and only call the 'az ml model create' command if the condition is met.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the 'condition' parameter in the 'model create' command.
Why it's wrong here
This parameter does not exist.
- ✗
Use an 'Azure Function' to trigger registration.
Why it's wrong here
This is an external dependency and not the standard orchestration pattern.
- ✗
Configure a 'ValidationThreshold' in the registry.
Why it's wrong here
No such feature exists.
- ✓
Implement logic in the pipeline to gate the registration step.
Why this is correct
Pipeline orchestration is required for conditional steps.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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