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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have a Microsoft Foundry project that serves a high-volume chat app.
Most requests are simple FAQs, but some require advanced reasoning.
You need to reduce costs and latency for common queries, without degrading the quality of the responses to complex questions.
What should you do?

A) Route all the requests to a smaller model.
B) Route all the requests to the most capable model.
C) Increase the value of the max_tokens parameter for all the requests.
D) Use a model cascade that routes the requests to different models.

2. You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?

A) Enable response streaming.
B) Switch to Retrieval Augmented Generation (RAG).
C) Add a reflection pass before the responses are returned.
D) Decrease the value of the temperature parameter.

3. You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
* Multiple client applications must use the same search configuration.
* A security policy must prevent key-based authentication.
* Administrative effort must be minimized.
What should you do?

A) Create a custom HTTP connection in Foundry and manually configure Azure AI Search endpoints per application.
B) Enable a managed identity for each application and call Azure AI Search directly.
C) Configure an Azure AI Search connection in Project1 and reference the connection in each application.
D) Call Azure AI Search directly from each application by using Microsoft Entra authentication.

4. Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a multimodal Al generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure protected material detection.
Does this meet the goal?

A) No
B) Yes

5. You have a Microsoft Foundry project that generates product marketing images from text prompts.
After publishing several images, the legal team at your company identifies a competitor ' s logo on a sign in the background of an image.
You need to remove only the logo, while preserving the rest of the image.
What should you do?

A) Modify the original prompt to exclude brand names.
B) Increase the prompt guidance strength.
C) Apply a mask-based inpainting edit to the part of the image that contains the logo.
D) Rerun the prompt by using a different random seed.

Solutions: