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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. You create a workspace by using Azure Machine Learning Studio.
You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio.
You need to reset the state of the notebook.
Which three actions should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A) Navigate to another section of the workspace.
B) stop the current kernel.
C) Change the compute.
D) Change the current kernel.
E) Reset the compute.

2. Multiple teams need access to approved models with version tracking, lineage, and governance controls. Models must be discoverable and reusable across projects. What Azure ML feature should you use?

A) Blob storage containers
B) Model registry
C) Data lake
D) Git repositories

3. Drag and Drop Question
You have an existing GitHub repository containing Azure Machine Learning project files.
You need to clone the repository to your Azure Machine Learning shared workspace file system.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

4. A product team is building a customer support assistant that must respond consistently across multiple channels.
Early testing shows that small wording changes in prompts cause large differences in tone and factual accuracy.
The team needs prompts that are reliable, reusable, and adaptable across multiple use cases without retraining the underlying model.
You need to design prompts that improve response quality while remaining flexible for future changes.
Which two actions should you perform? Each correct answer presents part of the solution.
(Choose two.)
NOTE: Each correct selection is worth one point.

A) Increase the temperature setting to encourage creativity.
B) Apply prompt transformations to separate system instructions from user input.
C) Fine-tune the model for each conversational variation.
D) Use the system prompt to establish the role, tone, and style.
E) Repeat the instructions at the end of the system prompt.

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

Solutions: