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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

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

2. 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) Yes
B) No

3. You have a Microsoft Foundry project that contains an agent. The agent has a Model Context Protocol (MCP) tool that queries a knowledge base stored in Azure AI Search.
Some agent runs return answers from the base model without invoking the knowledge base, which results in responses without grounded citations.
You are provided with the following code snippet that runs the agent.
run = project_client.agents.runs.create_and_process(
thread_id=thread.id,
agent_id=agent.id,
)
You need to add the correct tool_choice parameter to the code to deterministically force the agent to invoke the MCP tool on each run.
What should you add?

A) tool_choice={ " type " : " knowledge_base " }
B) tool_choice ={ " type " : " mcp " }
C) tool_choice={ " required " }
D) tool_choice={ " auto " }

4. You have a customer support agent built by using the Microsoft Foundry Agent Service. The agent calls an Azure OpenAl model deployment.
During load testing, calls intermittently fail and return an HTTP 429 rate limit exceeded error.
You need to handle throttling to reduce call failures and improve reliability under load. The solution must remain within the service and model limits.
What should you do?

A) Reduce the number of registered tools.
B) Create a new thread and retry the calls immediately.
C) Split uploaded content into smaller files.
D) Implement a retry policy that uses exponential backoff and jitter.

5. You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index.
You need to connect to the index to meet the following requirements:
* Complex questions must retrieve information from multiple chunks.
* Multi-turn conversations must influence retrieval planning.
* Retrievals must run in parallel to reduce latency.
Which retrieval approach should you use?

A) iterative retrieval
B) classic Retrieval Augmented Generation (RAG)
C) agentic Retrieval Augmented Generation (RAG)
D) chain of thought

Solutions: