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| Section | Objectives |
|---|---|
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Text analytics and summarization - Language understanding and intent recognition |
| Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
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NEW QUESTION # 109
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?
Answer: B
NEW QUESTION # 110
Drag and Drop Question
You have a web app that uses Azure AI Search.
When reviewing activity you see greater than expected search query volumes. You suspect that the query key is compromised.
You need to prevent unauthorized access to the search endpoint and ensure that users only have read only access to the documents collection. The solution must minimize app downtime.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Enforces Read-Only Permissions: Query keys are specifically designed to provide read-only access to the documents collection of an index. Admin keys provide full read-write administrative privileges and should never be distributed to consumer-facing applications.
Zero Downtime: Azure AI Search lets you generate up to 50 individual query keys. Creating a new one allows the app to stay online throughout the entire key rotation process Reference:
https://learn.microsoft.com/en-us/azure/search/search-security-api-keys
NEW QUESTION # 111
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?
Answer: A
Explanation:
The correct selection is D . In Microsoft Foundry Agent Service, tool_choice is the runtime control used to influence whether the model may answer directly or must invoke a tool. Microsoft's tool best-practice guidance states that auto lets the model decide whether to call tools, none prevents tool calls, and required means the model must call one or more tools. This directly addresses the issue where some runs answer from the base model and skip the knowledge base.
For an agentic retrieval solution backed by Azure AI Search through an MCP tool, Microsoft's tutorial states that setting tool_choice= " required " ensures the agent always uses the knowledge base tool when processing queries. This produces grounded answers because the run is forced into tool invocation before responding.
auto is incorrect because it preserves the nondeterministic behavior already causing missing citations. { " type
" : " knowledge_base " } is not a valid Foundry tool-choice type. { " type " : " mcp " } describes an MCP tool type in some Responses API schemas, but the deterministic guarantee for this agent run scenario is the required tool-call mode. Reference topics: Microsoft Foundry Agent Service, MCP tools, Azure AI Search agentic retrieval, tool_choice, and grounded citations.
NEW QUESTION # 112
You have a Microsoft Foundry project that contains an agent used by the financial analysts at your company.
You need to optimize the agent workflow by providing additional data access and processing capabilities. The solution must meet the following requirements:
* Ensure that the agent can perform calculations during conversations
* Ensure that the agent can access up-to-date information from public websites.
* Ensure that the agent can retrieve information from documents uploaded directly to the agent.
What should you use for each requirement? To answer, drag the appropriate tools to the correct requirements.
Each tool may be
used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Access up-to-date information from public websites: Grounding with Bing Search Perform calculations during conversations: Code interpreter Retrieve information from documents uploaded directly to the agent: File search The correct tool for public, current web information is Grounding with Bing Search . Microsoft Foundry Agent Service identifies Grounding with Bing Search as the built-in tool that enables an agent to access and return information from the internet, which fits the requirement for up-to-date public website data. ( learn.
microsoft.com )
For calculations during conversations, use Code interpreter . Microsoft's Foundry guidance states that Code Interpreter enables an agent to run Python code in a sandboxed execution environment and solve data analysis and math tasks iteratively. This is the correct fit for financial analysts who need calculations, analysis, and potentially chart generation during the conversation.
For documents uploaded directly to the agent, use File search . Microsoft describes File Search as the tool that enables Foundry agents to search through documents, retrieve relevant information, and augment model responses with knowledge from uploaded files such as PDFs, Word documents, and proprietary content.
Computer use is for interacting with graphical applications, not calculation or document retrieval. Microsoft Fabric is for enterprise data and analytics integration, not direct uploaded document retrieval. Reference topics: Foundry Agent Service tools, Code Interpreter, File Search, and Grounding with Bing Search.
NEW QUESTION # 113
You need to ensure that the marketing department can generate videos by using the model deployed to Project2. now should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Call client.videos.create() to submit the prompt and start a video-generation job against the model deployment. Video generation is asynchronous: the initial response represents a job and contains properties such as its unique ID and current status rather than immediately returning the completed video. Microsoft's official Python example creates the job by using client.videos.create(model=..., prompt=...).
The application must then poll the job. After waiting between requests, client.videos.retrieve(video.id) obtains the latest state of that specific job. Polling continues until the status becomes completed, failed, or cancelled.
list would enumerate jobs rather than refresh the selected job, while download_content is used only after successful completion to retrieve the generated video file.
To comply with Contoso's prohibition on API keys, the client should use a Microsoft Entra bearer-token provider. The OpenAI Python client accepts this provider through its api_key constructor parameter even though no static API key is used.
Study Guide alignment: design and implement video-generation solutions and integrate generative workflows through Foundry SDKs.
NEW QUESTION # 114
......
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