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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
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126. Frage
You process vendor onboarding PDFs containing tables and hyperlinks. The extracted content is indexed and supplied to an agent in Markdown.
You need Markdown with layout and semantic structure optimized for RAG.
Antwort: C
Begründung:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use prebuilt-documentSearch . Microsoft ' s current Content Understanding documentation explicitly classifies prebuilt-documentSearch as a Retrieval Augmented Generation analyzer .
RAG analyzers extract content with layout represented as Markdown and perform semantic analysis to enhance retrieval quality for downstream applications. prebuilt- documentSearch extracts document elements including paragraphs, tables, and figures and produces layout-preserving structured Markdown optimized for indexing and retrieval. Microsoft Learn prebuilt-layout is useful when the principal requirement is document-layout extraction, but the question explicitly requires both layout and semantic structure optimized for RAG . That wording maps directly to the RAG analyzer rather than the base layout analyzer.
prebuilt-read primarily addresses basic textual extraction, while prebuilt-documentFieldSchema serves field
/schema-oriented scenarios rather than producing RAG-optimized document representations.
Therefore, although the supplied file displays C (prebuilt-layout) , the verified current Microsoft Learn answer is B (prebuilt-documentSearch) . Microsoft ' s SDK documentation also describes prebuilt- documentSearch as preserving layout, detecting tables and figures, and generating structured Markdown specifically for RAG. Microsoft Learn
127. Frage
You have a Microsoft Foundry project that contains an agent named PaymentAgent.
PaymentAgent includes a function tool that issues customer refunds by using an external API.
You are creating a workflow in YAML.
You need to ensure that the workflow pauses for human approval and continues with the refund step only after approval is granted.
How should you complete the workflow definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
type: ask_question
condition: approval == " approved "
The approval step must use type: ask_question because the workflow must pause and wait for a human response before the refund execution proceeds. Microsoft Foundry workflows support human-in-the-loop patterns where the workflow asks the user a question and awaits input before continuing; this pattern is explicitly intended for approval requests and clarifying questions. The workflow guidance also identifies workflows as declarative sequences that orchestrate agents and business logic, including branching logic and human-in-the-loop steps.
The refund execution step must use condition: approval == " approved " so that the second invocation of PaymentAgent runs only when the approval response matches the required approval value. Using true would always execute the refund, bypassing the approval control. Using propose_refund.output != null would only confirm that the first agent step produced output; it would not prove that a human approved the refund.
data_transformation is also incorrect for the approval node because it sets or parses values rather than pausing for user input. Reference topics: Microsoft Foundry workflows, human-in-the-loop workflow pattern, YAML workflow editing, agent orchestration, conditional execution, and workflow approval gates.
128. Frage
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 dYouloyed 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.
Antwort:
Begründung:
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.
129. Frage
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?
Antwort: A
Begründung:
To ensure Microsoft Foundry automatically injects the API key from your project connection whenever the OpenAPI tool is invoked, your OpenAPI 3.0 specification must explicitly include a securitySchemes component mapping to the exact header name, and a global or operation-level security requirement referencing that scheme.The orchestrator matches the name field in the specification against the key stored inside your project's custom connection.
1. Required OpenAPI 3.0 Configuration
You must add both the components.securitySchemes block and the security block to your specification file:
openapi: 3.0.0
info:
title: External Weather Service
version: 1.0.0
paths:
/weather:
get:
operationId: getWeather
responses:
'200':
description: Successful weather retrieval
# 1. Define the security scheme in the components section
components:
*-> securitySchemes:
weatherApiKey: # Arbitrary logical identifier for this scheme
*-> type: apiKey
in: header
name: X-Weather-API-Key # MUST match the "key" name configured in your Foundry Connection
# 2. Apply the security requirement globally (or inside individual operations) security:
- weatherApiKey: [] # Instructs Foundry to enforce this scheme on the API requests in: header: Explicitly instructs the Foundry proxy layer to attach the credential value to the HTTP request headers (rather than as a query parameter).name: This string value is the exact HTTP header key (e.g., X-Weather-API-Key or Authorization). Crucially, this value must identically match the "Key" property given to the secret in your Microsoft Foundry Custom Connection.
security: Actively triggers the authentication workflow for the tool's endpoints. Without this block, Microsoft Foundry treats the API call as anonymous and strips out connection values.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/openapi
130. Frage
You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?
Antwort: B
131. Frage
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