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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have a Microsoft Foundry project that contains a customer support agent. The agent calls an internal knowledge API tool before generating responses.
Users report the following issues:
* Some requests take more than 15 seconds to complete.
* Some responses are incorrect, even when the knowledge API returns the expected data.
You need to inspect individual agent runs to view the ordered sequence of large language model (LLM) calls, tool invocations, and timing information.
Which observability capability should you use?

A) safety metrics
B) tracing
C) token usage
D) monitoring

2. You have an Azure Speech in Foundry Tools resource that hosts a custom speech to text model deployed to a custom endpoint. An agent uses the endpoint to perform real-time speech recognition.
You are approaching the expiration date of the custom speech to text model.
What is the expected behavior when the model expires?

A) The custom model will be deleted automatically when the model expires.
B) Speech recognition requests will fall back to the most recent base model for the same locale.
C) Speech recognition requests will continue to use the expired custom model until the model is removed manually.
D) Speech recognition requests will return a 4xx error until a new custom model is deployed.

3. You have a Microsoft Foundry project that contains an agent.
The agent ingests scanned PDF vendor invoices that contain tables and embedded QR codes.
The agent must preserve the PDF layout in the extracted output to ensure that downstream processing can reference sections and tables.
You plan to call Azure Content Understanding in Foundry Tools.
You need to extract content and layout elements and detect QR codes without requiring a language model deployment.
Which built-in analyzer should you use?

A) prebuilt-documentSearch
B) prebuilt-read
C) prebuilt-layout
D) prebuilt-documentFieldSchema

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 a prompt shield for documents.
Does this meet the goal?

A) No
B) Yes

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

Solutions: