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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question 1

Hotspot Question
A team is preparing a generative AI application for production deployment. The application generates structured responses that must be evaluated for quality before each release.
The organization requires repeatable evaluation results that can be compared across builds and environments.
You need to configure evaluation inputs so quality metrics can be reliably calculated across test runs.
How should you prepare the evaluation inputs? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 2

A company requires that only models meeting predefined performance thresholds are registered and deployed. The solution must be fully automated within the ML workflow. What should you implement?

A. GitHub pull request checks
B. Azure Monitor alerts
C. Conditional logic in pipeline
D. Manual approval gate

Question 3

Hotspot Question
A regression model is deployed to a production online endpoint and is actively serving predictions.
Business stakeholders report that the model's prediction quality has changed.
The endpoint remains available and responsive.
You need to determine which metric to examine to validate each quality concern.
Which metric should you review for each concern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 4

Hotspot Question
You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy for an Azure ML Python SDK v2-based model training.
The policy uses a slack_factor set to 0.1, an evaluation interval set to 1, and an evaluation delay set to 5.
You need to evaluate the outcome of the early termination policy.
What should you evaluate? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 5

You use Azure Machine Learning to train a model.
You must use Bayesian sampling to tune hyperparameters.
You need to select a learning_rate parameter distribution.
Which two distributions can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Choice
B. QNormal
C. Uniform
D. Normal
E. LogUniform

Solutions: