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Microsoft Developing AI Apps and Agents on Azure Sample Questions:

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

2. You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
Sometimes, mixed-language segments result in incomplete or incorrect translations.
You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
What should you do before sending the segments to Translator?

A) Enable automatic language detection for the translation request.
B) Split the mixed-language segments into single-language segments and translate each segment separately.
C) Use document translation to translate the entire transcript as a single document.
D) Specify English as the source language in the translation request for all the segments.

3. You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions.
The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements:
. Support turn-taking dynamics, where the agent begins to generate the speech output before the user finishes speaking.
. Operate with low latency to maintain a conversational experience.
You need to enable both speech to text and text to speech in a real-time agent interaction.
What should you do?

A) Use speech translation to convert the audio into another language and return the translated text.
B) Use an embeddings model to encode the audio, and then decode the audio into text and speech.
C) Use real-time speech to text for incoming audio and text to speech for agent responses.
D) Use batch transcription to convert the audio input and return text responses from the agent.

4. You have a Microsoft Foundry project.
You plan to build a customer support solution that contains an agent. The solution must meet the following requirements:
* Provide accurate, context-aware responses grounded in internal product documentation stored in Azure AI Search.
* Require deep, multi-step reasoning across long contexts.
* Generate detailed natural language responses.
Which type of model should you use to power the agent?

A) a small language model (SLM)
B) a large language model (LLM)
C) a multimodal model
D) a key phrase extraction model

5. 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) Implement a retry policy that uses exponential backoff and jitter.
C) Split uploaded content into smaller files.
D) Create a new thread and retry the calls immediately.

Solutions: