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| Section | Weight | Objectives |
|---|---|---|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
| Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
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質問 # 137
You have a customer support agent that uses the Microsoft Foundry Agent Service.
Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following:
* Multi-turn continuity within the session
* Cross-session continuity for the same case
* Access to the full interaction history, including user messages, agent messages, tool calls, and tool outputs You need to ensure that the agent automatically reloads the complete history on each new turn.
What should you do?
正解：A
解説：
The correct approach is to create and reuse a conversation by storing the conversation's ID and supplying that ID on subsequent requests . In Microsoft Foundry Agent Service, conversations are durable objects with unique identifiers that can be reused across sessions. The official runtime guidance states that conversations store items, including messages, tool calls, tool outputs, and other data, and are intended for multi-turn continuity, cross-session continuity, and inspection of what happened over time. This directly satisfies the requirement to resume the same support case days later with the full historical context.
Persisting only the final model response is insufficient because it loses the full interaction chain, especially tool calls and tool outputs that may be essential to case state. Memory summarization is also not the best fit because the requirement asks for the complete history, not a compressed representation that may omit details.
Reusing the conversation ID allows Foundry to maintain the conversation server-side so the next turn can reuse prior context without the client manually rebuilding prompts. Reference topics: Foundry Agent Service runtime components, conversations, conversation items, multi-turn continuity, cross-session continuity, and tool output history.
質問 # 138
Hotspot Question
You develop a test method to verify the results retrieved from a call to the Azure Vision in Foundry Tools API. The call is used to analyze the existence of company logos in images. The call returns a collection of brands named brands.
You have the following code segment:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Box 1: Yes
The code segment correctly filters for and displays the name (and coordinates) of each detected brand only if the model's confidence score is 75 percent or higher.The expression if brand.confidence >= 0.75 guarantees that only brands meeting or exceeding this threshold are printed.
Box 2: Yes
The code segment will display the coordinates. Specifically, it prints the x and y values of the rectangle's top-left corner alongside its width (w) and height (h) for any detected brand with a confidence score equal to or greater than 0.75 (75%).
The provided code uses the properties directly to extract the bounding box:
brand.rectangle.x and brand.rectangle.y: The coordinates of the top-left corner of the bounding box.
brand.rectangle.w and brand.rectangle.h: The width and height of the bounding box.
Box 3: No
See Box 2 above.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-brand-detection
質問 # 139
Case Study 1 - Contoso, Ltd
Overview
Company Information
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
Existing Environment
Identity Environment
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
Generative Environment
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
- Agent1 uses a base model deployment.
- A safety evaluation pipeline is NOT enabled.
- Tool invocation approval workflows are NOT enabled.
- Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment
Contoso stores product-related information in Azure resources that support AI applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statements
Contoso identifies the following issues:
- Agent1 has only general knowledge of the Contoso products.
- A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
- Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
- The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirements
Planned Changes
Contoso plans to implement the following changes:
- Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
- Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
- Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
- Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
- Complete the development of the video creation solution.
Technical Requirements
Contoso identifies the following technical requirements:
- The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
- The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
- Responses generated by using the product sheet information must be relevant, complete, and accurate.
- Agent1 must be able to use the product sheets to answer natural language questions about product details.
- The model version used by Agent1 must remain consistent to ensure stable responses.
- The data processed by the model must remain within the EU.
Security and Compliance Requirements
Contoso identifies the following security and compliance requirements:
- API keys must NOT be used to access Foundry-deployed models.
- Access to the Azure resources must follow the principle of least privilege.
- The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
- Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
- Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
- Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
- The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Requirements
Contoso identifies the following business requirements:
- Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
- Agent1 must answer questions only about the products sold by Contoso.
You need to configure an indexing pipeline for Agent1 to retrieve the relevant product information in storage1. The solution must meet the technical requirement.
Which two built-in skills should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：A、E
解説：
The most essential skills for this scenario are Azure OpenAI Embedding and Text Split.
Azure OpenAI Embedding: This skill is critical for generating the vector representations (embeddings) of your text, which directly enables the required vector search capability.
Text Split: This skill is essential because LLMs and embedding models have strict token limits.
Breaking large product detail sheets into smaller chunks ensures the text fits into the embedding model and improves the accuracy of semantic search.
Incorrect:
[Not B]
Entity Recognition: This extracts specific entities like names, dates, or locations. While helpful for advanced filtering, it is not a foundational requirement to enable basic semantic or vector search.
[Not D]
Merge: This skill combines text from multiple fields into a single string. Since product sheets are already unified documents, splitting and chunking them is the priority rather than merging separate fields.
[Not E]
Language Detection: This identifies the language of the input text. Unless your product sheets are completely multilingual and require conditional routing to different language models, this skill is secondary.
[Not F]
Key Phrase Extraction: This pulls out main talking points or keywords. This is primary used for traditional keyword tagging or basic search indexing, whereas your requirement specifically dictates vector and semantic-based retrieval.
Scenario:
Technical Requirements;
*-> The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
Data environment: The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
Planned changes: Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
Reference:
https://www.rheininsights.com/blog/en/Retrieval+Augmented+Generation+with+Azure+AI+Search
+and+Atlassian+Confluence.php
質問 # 140
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?
正解：B
解説：
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
質問 # 141
You have an Azure subscription that contains an Azure Language in Foundry Tools service resource. You need to identify the URL of the REST interface for the Language service. Which blade should you use in the Azure portal?
正解：C
解説：
The Keys and Endpoint blade displays the resource-specific endpoint URL used to construct REST API requests to Azure Language in Foundry Tools. It also provides the resource access keys when local key-based authentication is enabled. Microsoft's Azure Language documentation directs administrators to open the resource in the Azure portal and select Keys and Endpoint from the left menu to obtain the endpoint and credentials required for API calls.
The endpoint normally forms the base URI for the service. The application appends the operation-specific route and API version required by features such as sentiment analysis, named entity recognition, entity linking, or conversational language understanding. The key may then be supplied in the request's authentication header, although Microsoft Entra ID and managed identities are recommended for supported production workloads because they avoid embedding secrets in application code.
The Networking blade controls public access, firewalls, virtual networks, and private endpoints. Identity manages the resource's managed identity. Properties presents general resource metadata but is not the designated location for retrieving the REST API endpoint and access keys.
Study Guide alignment: plan and manage Azure AI resources, configure service access, and integrate Foundry Tools through their APIs and authentication mechanisms .
質問 # 142
......
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