Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. 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?

A) Yes
B) No

2. You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible AI dashboard to assess the model.
You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
What should you do first?

A) Deploy the model to a managed online endpoint.
B) Register the model with the workspace.
C) Convert the model from the MLflow format to a custom format.
D) Create the model explanations.

3. A company requires that only models meeting predefined performance thresholds are registered and deployed. The solution must be fully automated within the ML workflow. What should you implement?

A) Azure Monitor alerts
B) Conditional logic in pipeline
C) Manual approval gate
D) GitHub pull request checks

4. You have an Azure subscription named Sub1 that contains an Azure Machine Learning workspace named Workspace1. Workspace1 contains the following assets:
- a registered MLflow model named Model1
- an online endpoint named Endpoint1
Outbound network connectivity from Endpoint1 is blocked.
You need to deploy Model1 to Endpoint1.
What should you do first?

A) In Workspace1, create a package.
B) In Sub1, create an Azure Machine Learning registry.
C) In Sub1, create a private endpoint.
D) In Workspace1, create a linked service.

5. Hotspot Question
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions: