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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Drag and Drop Question
A team deploys a classification model to production and monitors performance and data changes.
The team wants to ensure that significant drops in prediction accuracy automatically trigger the following:
- Stakeholders must be notified of the drops.
- Retraining must be initiated when thresholds are exceeded
You need to configure monitoring to meet the requirements.
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.

2. 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) Reset the compute.
C) Change the current kernel.
D) stop the current kernel.
E) Change the compute.

3. 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.
You manage a Retrieval-Augmented Generation (RAG) application built on Microsoft Foundry.
The application retrieves documents from an indexed knowledge base and generates answers for internal users.
Recent feedback indicates that answers are fluent but sometimes include information that is not supported by the documents that were retrieved.
You need to evaluate whether a proposed change improves RAG answer quality by using supported and measurable techniques.
Solution: Compare embedding vector dimensions used by the retrieval pipeline before and after the change.
Does the solution meet the goal?

A) Yes
B) No

4. You deploy a model to production but do not have labeled data available for evaluating prediction accuracy. However, you must monitor model health continuously. What is the BEST strategy?

A) Monitor accuracy metrics
B) Disable monitoring
C) Perform manual evaluation only
D) Use data drift detection

5. You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image.
A new version of the Docker image is available.
You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2.
What should you do?

A) Use the create_or_update method to change the tag of the image.
B) Change the description parameter of the environment configuration.
C) Use the Environment class to create a new version of the environment.
D) Modify the conda_file to specify the new version of the Docker image.

Solutions: