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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement agentic solutions | 20-25% | - Manage agent operations
|
| Topic 2: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 3: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 4: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 5: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
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NEW QUESTION # 64
You have a Microsoft Foundry project named Project1 that contains the following:
- An OpenAPI tool that calls an external API
- A project connection named Connection1 that stores the API key of the external API When an agent calls the OpenAPI tool, the API returns a 401 unauthorized error, and traces show that the API key header is NOT being sent.
You need to ensure that the OpenAPI tool automatically includes the API key from Connection1 on all requests.
What should you do?
Answer: C
Explanation:
To make sure that the OpenAPI tool automatically includes the API key from the connection on all requests, you should explicitly define the security schemes and security requirements in your OpenAPI specification file.
Incorrect:
[Not D]
Even if you successfully link the tool to the custom connection in the portal UI, the Microsoft Foundry agent service will intentionally redact or withhold the API key if it cannot find a corresponding matching structure in the tool's code definition.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/openapi
NEW QUESTION # 65
You have an Azure subscription that contains an Azure OpenAI resource.
You plan to build an agent by using the Azure AI Agent Service. The agent will perform the following actions:
- Interpret written and spoken questions from users.
- Generate answers to the questions.
- Output the answers as speech.
You need to create the project for the agent.
What should you use?
Answer: B
Explanation:
Azure AI Foundry is a platform for designing, customizing, managing, and supporting AI applications and agents. It acts as an AI app factory, providing a unified environment with tools, models, and deployment pipelines for various AI tasks. It enables teams to build and operate AI solutions, including those powered by generative AI, while ensuring security, governance, and cost-efficiency.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-azure-ai-foundry
NEW QUESTION # 66
You are planning a Microsoft Foundry project named Project1 that will contain multiple agents. Each agent will access the same Azure Al Search resource.
You need to recommend a solution to centrally manage the Azure Al Search credentials within Project1. The solution must be implemented across all the agents.
What should you recommend?
Answer: D
Explanation:
The correct recommendation is B. Add a connection to the Azure AI Search resource . Microsoft Foundry project connections are used to centrally define access from a project to external resources, including Azure AI Search. The official connection guidance states that you can add a connection by selecting an external service such as Azure AI Search , choosing the resource, and selecting the authentication method for that resource. This creates a reusable project-level configuration rather than requiring each agent to store or duplicate search credentials independently.
For Foundry agents that use Azure AI Search, the Azure AI Search tool requires a project_connection_id, which is the resource ID of the project connection to Azure AI Search. This allows multiple agents to reference the same managed connection while using the configured endpoint and authentication settings consistently. RBAC may be part of a keyless authentication design, but it does not by itself create a centrally managed Foundry credential configuration. Disabling key-based access improves security posture but does not connect the agents. A managed private endpoint addresses network isolation, not credential centralization.
Reference topics: Microsoft Foundry project connections, Azure AI Search tool, project connection IDs, authentication, and agent grounding.
NEW QUESTION # 67
An agent must call your organisation's internal inventory system, which is exposed as a set of tools. You want the tool definitions maintained centrally and reusable across several agents rather than redefined in each agent. Which Foundry capability fits?
Answer: A
Explanation:
A Model Context Protocol (MCP) server exposes a reusable, centrally maintained set of tools that any agent can connect to, and Foundry Agent Service supports adding MCP servers from the tools catalogue. This is the standard pattern for connecting agents to external systems through shared tool definitions.
NEW QUESTION # 68
You are creating an enrichment pipeline that will use Azure Al Search. The knowledge store contains unstructured JSON data and the text from scanned PDF documents.
Which projection type should you use for each data type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Use an object projection for the unstructured JSON data. Object projections store a JSON representation of an enrichment tree node in an Azure Blob Storage container. They preserve hierarchical fields and complex structures, making them appropriate when enriched document content must remain available as a complete JSON object rather than being decomposed into relational rows. Microsoft describes object projections as JSON representations that can be sourced from nodes in the enrichment tree.
For the scanned PDF content, use a file projection . During document cracking and OCR processing, scanned pages are represented through the /document/normalized_images/* collection. File projections write these binary normalized images to Blob Storage, preserving the page assets from which the text is extracted.
Microsoft specifies that file projections operate only on normalized images and contain binary data rather than JSON.
A table projection is intended for row-and-column structures used by analytical tools such as Power BI. It is not the appropriate choice for preserving hierarchical JSON or scanned-document image files.
Study Guide alignment: configure Azure AI Search enrichment pipelines, skillsets, knowledge stores, and table, object, and file projections .
NEW QUESTION # 69
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