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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A company has multiple data science teams working on separate machine learning projects.
The company requires models to be auditable, reusable, and governed centrally across teams.
The models must allow team-level isolation for billing.
You need to establish the foundation for governed machine learning operations.
Which action should you perform first?

A) Register shared datasets in a central storage account.
B) Create a shared Azure Machine Learning workspace.
C) Create a shared hub workspace and project workspaces for each team.
D) Create a resource group for shared machine learning assets.

2. Drag and Drop Question
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action 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.

3. A Retrieval-Augmented Generation (RAG) solution returns incomplete answers because relevant content is inconsistently retrieved from the knowledge source.
You need to improve RAG accuracy without changing the embedding model currently in use. You need to achieve this goal while minimizing operational costs.
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) Implement an optimized re-ranker.
B) Optimize the length of embedding vectors.
C) Increase token limits for all requests.
D) Tune chunk size and overlap to match content structure.

4. Hotspot Question
You are monitoring a fine-tuned large language model deployed in Microsoft Foundry.
You evaluate the model before and after fine-tuning by using the same evaluation dataset.
You review the following evaluation results:

You need to determine whether the fine-tuned model shows improved performance without introducing regression. For each of the following statements, select Yes if the statement is true.
Otherwise, select No.
NOTE: Each correct selection is worth one point.

5. Hotspot Question
A team is provisioning a new Azure Machine Learning workspace for a production project.
The workspace must support secure secret storage and operational monitoring. The team requires the workspace to be created with the correct dependent resources to meet security and monitoring requirements.
You need to configure the required dependencies when the team creates the workspace.
Which resources should you associate with the workspace? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions: