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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?

A) Textual similarity
B) Grounded ness
C) Fluency
D) Coherence

2. You manage an Azure Machine Learning workspace.
You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.
Which parameter should you use?

A) build
B) image
C) conda_file
D) properties

3. 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.

4. An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?

A) Create a shared Azure Machine Learning environment that includes the asset.
B) Publish the asset as a pipeline component.
C) Publish the asset to an Azure Machine Learning registry.
D) Enable workspace-level Git integration and sync assets between repositories.

5. Drag and Drop Question
An organization is adopting Microsoft Foundry to support multiple generative AI projects across different teams. Microsoft Foundry platform administrators require centralized governance.
Development teams need isolated environments for experimentation and deployment.
Shared policies must be enforced consistently while allowing teams to work independently.
You need to configure the Microsoft Foundry environment to meet the requirements.
Which Microsoft Foundry components should you configure? To answer, move the appropriate configurations to the correct responsibilities. You may use each configuration 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.

Solutions: