logo
Exam overview and study plan

AI-300 Practice Questions

Machine Learning Operations Engineer Associate

Read questions, check verified answers and discuss the reasoning. Use reading options to shape your revision, or switch to the simulator for timed practice.

View 134 free questions

134 questions in this view

Page 1 of 23

Question 1

You need to configure access for the production chatbot solution.

How should you configure the settings? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.
SettingConfiguration
Authentication method
Prompt engineer permissions

Question 2

You need to organize the production repository to support the prompt versioning workflow of Northwind Traders.

How should you organize the repository? To answer, move the appropriate artifact organizations to the correct repository artifacts. You may use each artifact organization 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.
Artifact organizations
Repository artifact organization
Repository artifactArtifact organization
Agent instruction files
Files generated during evaluation

Question 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: Run an A/B test comparing the current configuration with the proposed change and measure groundedness and relevance scores for each variant.

Does the solution meet the goal?

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 4

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: Review user feedback comments collected after deployment to determine whether answers appear more accurate.

Does the solution meet the goal?

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

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.

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?

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.

Question 6

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?

Select an answer above, then submit your vote. Reading or practicing does not cast a vote.