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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 2: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 3: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 4: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 5: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
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NEW QUESTION # 19
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
Answer: B
Explanation:
The first step should be an Azure Content Understanding analyzer within Microsoft Foundry Tools.
This specific tool is designed to handle the complexities in this scenario--scanned text, complex tables, and multi-column layouts--by converting unstructured documents into a structured Markdown format. This preserves the document's original hierarchy and structural relationships, which is critical for accurate downstream reasoning by an agent.
Incorrect:
[Not A]
Azure Language is designed for text-based natural language processing (NLP) like sentiment analysis or PII redaction on raw text strings. It cannot parse scanned images, optical character recognition (OCR), tables, or complex multi-column visual layouts.
[Not C]
The first step in this specific Microsoft Foundry agent configuration should not be an Azure OpenAI Responses API call.While multimodal models can read images, they frequently hallucinate data, fail to capture complex multi-column reading orders, and degrade table structures when processing dense, mixed-format documents.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/elements
NEW QUESTION # 20
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
* Authenticates by using a Microsoft Entra managed identity
* Sends prompts to a deployed model by using the Azure OpenAI Responses API How 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:
credential = DefaultAzureCredential
response = openai_client.responses.create
The correct authentication class is DefaultAzureCredential because the requirement is Microsoft Entra-based authentication, and this credential supports managed identity in hosted Azure environments. The Microsoft Foundry quickstart for Python shows the project client being created with AIProjectClient (endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential()), which enables the Foundry SDK to authenticate without using API keys or embedded secrets. The same guidance shows creating an OpenAI- compatible client from the project by calling project.get_openai_client().
The correct Responses API method is create because the application must send a new prompt to the deployed model and receive generated output. Microsoft's Foundry quickstart demonstrates the exact pattern: response
= openai.responses.create(model= " gpt-5-mini " , input= " ... " ), followed by reading response.output_text.
The retrieve operation is used to fetch an existing response, not submit a new inference request, and compact is not the correct method for generating a model response. AzureKeyCredential would violate the Microsoft Entra managed identity requirement, while ClientSecretCredential uses an application secret rather than managed identity. Reference topics: Microsoft Foundry SDK, AIProjectClient, Microsoft Entra authentication, DefaultAzureCredential, and Azure OpenAI Responses API.
NEW QUESTION # 21
Hotspot Question
You have a Microsoft Foundry project that contains two agents named PolicyWriter and RskReviewer.
PolicyWriter generates daft updates for customer polices, and RiskReviewer reviews the drafts.
In the visual builder, you need to create a workflow that meets the following requirements:
- Finalizes low-risk updates without manual intervention
- Ensures predictable execution across the agents
- Requires user approval for highs updates
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each comet selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: The human-in-the-loop template that pauses execution of the workflow for input Human-in-the-loop template is correct. This pattern allows the system to run automatically for predictable, node-by-node agent execution while explicitly pausing the workflow when a high-risk condition is met to wait for human intervention.
Box 2: Add a Condition statement
Condition statement is correct. You must evaluate the risk level (low vs. high) before deciding whether to finalize the policy. A condition node checks the risk score output from RiskReviewer. If it is low, it routes to auto-finalization. If it is high, it routes to an approval step.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/building-human-in-the-loop-ai-workflows-with-microsoft-agent-framework/4460342
NEW QUESTION # 22
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: D
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 # 23
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?
Answer: A
Explanation:
The correct answer is agentic Retrieval Augmented Generation (RAG) because the requirements describe the agentic retrieval pipeline in Azure AI Search. Agentic retrieval is designed for chat and copilot scenarios where a user's request can be complex, conversational, and dependent on prior turns. Azure AI Search agentic retrieval uses an LLM-assisted planning stage to break a complex request into focused subqueries, allowing the system to retrieve grounding information from multiple chunks rather than relying on a single query path.
Microsoft's Azure AI Search guidance describes agentic retrieval as a multi-query pipeline for complex questions in chat and agent workflows, with subqueries that can include chat history for additional context.
This also satisfies the latency requirement because agentic retrieval runs the generated subqueries in parallel and then merges and reranks the best results for use by the generative model. Classic RAG is simpler and typically sends a single query to search, making it less suitable for multi-hop or conversational retrieval planning. Chain of thought is a reasoning technique, not an Azure AI Search retrieval approach, and iterative retrieval does not specifically provide the built-in query planning, conversation-aware retrieval, and parallel execution described here. Reference topics: Azure AI Search agentic retrieval, RAG with Azure AI Search, knowledge bases, query planning, and generative AI grounding.
NEW QUESTION # 24
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