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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question 1

You train models on GPU-enabled clusters but deploy them on CPU-based endpoints. Recently, inference failures occur due to incompatible dependencies. What should you do to ensure consistency?

A. Increase endpoint compute size
B. Define and reuse environment configurations
C. Use same compute for training and inference
D. Use batch endpoints

Question 2

Drag and Drop Question
You build and manage a model by using Azure Machine Learning workspace.
Before you deploy the model, you must create a Responsible AI dashboard in Azure Machine Learning studio. The dashboard must provide observation of the following:
- metrics that show real-world impact on an outcome of interest due to
taking a treatment policy
- examples with minimal changes to a particular data point such that
the model's prediction changes
You need to implement the components for the Responsible AI dashboard.
Which components should you implement? To answer, move the appropriate components to the correct observations. You may use each component once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Question 3

DRAG DROP
A team deploys a classification model to production and scores incoming customer data daily.
After several weeks, business stakeholders report unexpected changes in prediction behavior, even though the endpoint remains healthy.
You need to determine whether data drift is occurring and if it is, identify the appropriate actions.
Which action should you perform for each observed signal? To answer, move the appropriate actions to the correct observed signals. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Question 4

An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met.
Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics.
What should you do?

A. Review evaluation results manually after deployment.
B. Enable application tracing to collect runtime telemetry.
C. Monitor latency metrics during model inference.
D. Implement an evaluation step by using GitHub Actions.

Question 5

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
An organization provisions Azure Machine Learning workspaces for development, test, and production environments.
Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.
You need to deploy Azure Machine Learning resources in a consistent and controlled manner.
Solution: Create Azure Machine Learning workspaces manually in the Azure portal for each environment.
Does the solution meet the goal?

A. No
B. Yes

Solutions: