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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 work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Does the solution meet the goal?

A) No
B) Yes

2. Hotspot Question
A company is creating an internal tool that summarizes long meeting transcripts and extracts action items.
The model must:
- Process text inputs up to 200k tokens long.
- Generate concise summaries in seconds.
- Support interactive testing before integration into the app.
You need to select, deploy, and test a model that supports summarization with low latency.
How should you complete the configuration plan? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

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 in the review screen.
You have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.8 - AzureML kernel.
Does the solution meet the goal?

A) No
B) Yes

4. You manage an Azure Machine Learning workspace. You build a model for which you must configure a Responsible Al dashboard.
Based on what you learn from the dashboard, you must perform the following activities:
- Determine what must be done to get a desirable outcome from the
model.
- Identify the features that have the most direct effect on your
outcome of interest.
You need to select the components to use for the Responsible Al dashboard configuration.
Which two components should you add? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) error analysis
B) explanation
C) causal
D) counterfactuals

5. Drag and Drop Question
An organization uses Microsoft Foundry to develop generative AI projects that access shared Azure resources such as storage accounts and vector databases.
The organization s security policy requires eliminating secret key-based authentication and enforcing least-privilege access.
You must configure identity and access so that:
Services authenticate without stored credentials.
Permissions are scoped appropriately across projects and shared resources.
You need to configure the appropriate identity or access mechanism for each requirement.
What should you configure in Microsoft Foundry to meet each requirement? To answer, move the appropriate configuration mechanisms to the correct requirements. You may use each configuration mechanism 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.

Solutions: