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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?
You have a Microsoft Foundry project that contains an agent. The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables. You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page-number metadata. Users frequently ask questions that require the retrieval of specific table rows across the pages. You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk. How should you configure the ingestion job?
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 Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents. Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content. You need to improve response completeness. Solution: You increase the value of the max_tokens parameter. Does this meet the goal?
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 AI 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 image moderation to block unsafe content before processing the images. Does this meet the goal?
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?
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Speech in Foundry Tools. You fine-tune a baseline speech to text model for the en-us locale and publish the model. The agent calls the Speech to text REST API and returns an error message indicating that the project ID is invalid. You need to set the project property to the correct ID. To what should you set the project property?
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?
You have a Microsoft Foundry project named Project1 that contains an agent. The agent uses an OpenAPI 3.0 specification to call an external weather service. The weather service requires a key to be passed in an HTTP header. The key value is stored as a connection in Project1. You need to ensure that the key value from the connection is included automatically whenever the OpenAPI tool is invoked. What should you configure in the OpenAPI specification?
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?
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?