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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 4: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 5: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
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NEW QUESTION # 66
You have an Azure subscription that contains an Azure Al Foundry instance named AI1.
You have an app that automatically triages and resolves issues presented in the log files of a system.
You create an incident manager agent and a DevOps agent that collaborate to resolve the issues.
You need to ensure that the incident manager agent can assign work to the DevOps agent. The solution must minimize development effort.
What should you do?
Answer: C
Explanation:
Ensuring that an incident manager agent can assign work to a DevOps agent in Azure AI Foundry is primarily achieved through a Connected Agents configuration. This setup allows a "main" agent to delegate tasks to "specialized" agents via natural language or defined function calls.
Key Implementation Steps
1. Configure Connected Agents
Within the Azure AI Foundry portal, you must register the DevOps agent as a "Connected Agent" of the incident manager.
2. Define Tool-Based Delegation
Use the ConnectedAgentToolDefinition in the Azure Python SDK or C# SDK to programmatically link them. This exposes the DevOps agent to the incident manager as a callable "tool".
3. Implement Handoff Orchestration Patterns
Choose an orchestration pattern that fits the triage-to-resolve workflow.
4. Manage Context and State
5. Enable Permissions and Roles
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/connected-agents
NEW QUESTION # 67
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
Answer: A
Explanation:
To enhance response completeness in your Microsoft Foundry agent, you must intercept the retrieved documents and the generated summary within your backend application logic before returning the payload to the user.
1. Implement Completeness Verification Logic
Add a verification step in your orchestration code (e.g., in your Python/Semantic Kernel or LangChain pipeline) that compares the generated summary against the retrieved chunks.' Map Key Assertions: Extract main policy rules from retrieved text.Cross-Reference Entities: Verify all key entities are in the summary.
Check Scope Coverage: Ensure every retrieved document is represented.
Scan for Gaps: Identify critical missing constraints or exceptions.
2. Apply Application-Level Mitigation Strategies
If the verification step detects that the summary is incomplete, use your code to correct it before the final response leaves your system.
Reference:
https://dev.to/moonrunnerkc/how-i-built-a-verification-layer-for-copilot-clis-multi-agent-output-4b7h
NEW QUESTION # 68
You are building a web app named App1 that generates responses by using a model deployed to a Microsoft Foundry project named Project1.
Before sending the prompts to the model, App1 must retrieve documents by using Azure AI Search.
You need to integrate Project1 and App1. The solution must meet the following requirements:
* Multiple client applications must use the same search configuration.
* A security policy must prevent key-based authentication.
* Administrative effort must be minimized.
What should you do?
Answer: B
Explanation:
The correct solution is to configure an Azure AI Search connection in Project1 and reference that connection from each application. Microsoft Foundry project connections are intended to centralize external resource configuration for a project. The official Foundry guidance states that when adding a connection, you select the external service, such as Azure AI Search , and choose the authentication method for that resource.
This directly satisfies the requirement for multiple client applications to use the same search configuration instead of duplicating endpoints, indexes, and credentials in each app.
The security requirement is met by configuring the connection with keyless authentication. The Azure AI Search integration guidance specifies that a Foundry project connection requires the search endpoint and either key-based authentication or keyless authentication with Microsoft Entra ID . For keyless authentication, RBAC roles are assigned to the project's managed identity, eliminating hard-coded API keys.
Options A and C use secure identity-based access but force every client application to maintain its own Azure AI Search configuration, increasing administration and violating the shared-configuration requirement. Option B also increases administrative work and does not use the native Azure AI Search connection type. Reference topics: Microsoft Foundry project connections, Azure AI Search grounding, Microsoft Entra ID authentication, managed identities, and RBAC.
NEW QUESTION # 69
In mid-2026 you are starting a brand-new agentic application on Azure and want to build on the generally available, supported entry point for Foundry agents. Which API should you target?
Answer: A
Explanation:
The Responses API is the generally available single entry point for Foundry Agent Service and supports both prompt agents and hosted agents. It is the API Microsoft directs new agentic development towards.
NEW QUESTION # 70
You have a Microsoft Foundry project that contains three agents as shown in the following table.
You need to orchestrate the agents to ensure that the customer requests meet the following requirements:
- Support a deterministic, step-based process that uses conditional
branching and shared state across the agents.
- Optionally trigger a ticket action based on the triage result.
The solution must minimize development effort.
What should you include in the solution?
Answer: C
Explanation:
To fulfill your requirements while keeping development effort to an absolute minimum, you should leverage the native Microsoft Foundry Multi-Agent Workflows feature (built directly into the Foundry Agent Service and managed via the Foundry portal visual editor or declarative YAML files).Using this visual, low-code orchestration layer removes the need to write custom graph routing logic, state managers, or manual handoffs in code.
The minimum required architecture and features that must be included in your solution are structured below.
1. The Orchestration Layer: Declarative Workflow
Instead of writing a code-first orchestrator, you must define a Foundry Workflow Definition (YAML or Visual).
2. State Management: Shared Context Variables
3. Agent Configuration & Native Tooling
Reference:
https://devblogs.microsoft.com/foundry/introducing-multi-agent-workflows-in-foundry-agent-service/
NEW QUESTION # 71
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