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| Section | Weight | Objectives |
|---|---|---|
| Develop multi-agent solutions in Azure | 30-35% | - Implement agent memory, context management, and knowledge integration
|
| Secure, govern, and deploy multi-agent solutions | 20-25% | - Design and implement guardrails
|
| Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Implement observability and monitoring
|
| Architect multi-agent solutions | 15-20% | - Design logical architecture for multi-agent solutions
|
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63. Frage
You have a Microsoft Foundry multi-agent customer support solution. The solution includes a triage agent that uses Azure Al Search to retrieve customer-supplied HTML articles and Azure Functions to retrieve customer relationship management (CRM) case records. A custom guardrail is assigned directly to the agent.
Some retrieved HTML articles contain hidden instructions that attempt to override the triage agent ' s system message You need to configure guardrails to meet the following requirements:
* Evaluate the complete payload from the supported tools before the content is available to the agent.
* Prevent malicious instructions embedded in externally retrieved content from influencing the agent
* Stop processing when tainted data is detected and inform the security team.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Intervention point: Tool response; Risk: Indirect attacks.
The hidden instructions originate in retrieved HTML returned by supported tools, not in the user ' s direct prompt. Microsoft classifies that pattern as an indirect prompt-injection attack. The Tool response intervention point is specifically designed to inspect the complete payload returned from a tool before the content is stored in agent memory or used for subsequent reasoning. Configuring the Indirect attack control there with a blocking action prevents tainted retrieved data from steering the triage agent. Azure AI Search and Azure Functions are among the supported tools for tool-response moderation, which is important because both appear in the scenario. A Tool call control would inspect arguments sent to a tool rather than hostile content coming back from it. User-input attack detection likewise targets the wrong origin. The correct configuration is therefore Tool response plus Indirect attacks, with the operational response set to stop processing and alert the security workflow when malicious content is detected.
Official Microsoft reference: Microsoft Foundry guardrails - intervention points
64. Frage
You have a Microsoft Foundry multi-agent solution.
Historical chat logs are limited and include customer Personally Identifiable Information (Pll).
The agents frequently produce invalid arguments when they call APIs by using structured function calls.
You need to create an initial fine-tuning dataset to improve the API-call behavior. The solution must minimize privacy exposure.
How should you configure the pipeline? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Initial example source: Synthetic generation; Generation task type: Tool use.
The historical chat logs are both sparse and privacy-sensitive, so using them directly as the initial fine-tuning corpus creates unnecessary PII exposure. Microsoft Foundry synthetic-data generation is intended to create diverse training examples when production data is limited and can avoid carrying customer identifiers into the dataset. The specific behavior that needs improvement is structured API/function calling, so the generator should use the Tool use task type. Tool-use generation can use an API/OpenAPI definition to create conversations that include valid tool selection and parameter construction, which directly trains the failure mode described. A general Q & A generator would not systematically teach function-call schemas. Therefore Synthetic generation plus Tool use is the configuration that both targets invalid arguments and minimizes privacy exposure. At implementation time, the same rule should be expressed through the framework or service configuration rather than left only as a natural-language convention. That makes the behavior repeatable across runs, easier to test, and less sensitive to model variability.
Official Microsoft reference: Microsoft Foundry - synthetic fine-tuning data generation
65. Frage
You have a Microsoft Foundry agent built by using LangGraph. The agent retrieves policy content from a vector store. The LangGraph orchestration runs in Azure Container Apps, and the operations team uses Azure Monitor to inspect agent runs.
You discover that the vector store is sometimes unreachable during Azure regional maintenance windows You need to configure the tool ecosystem to ensure that the agent can answer policy questions during outages The solution must meet the following requirements:
* Use the project vector store without adding a local retrieval node for the primary path.
* Run the cached-policy lookup in the same compute environment as the LangGraph code.
* Emit agent, model, and tool spans that can be inspected in Azure Monitor How should you configure the tool ecosystem? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Corpus access: AgentServiceBaseTool wrapping FileSearchTool; Outage fallback: No listed option fully satisfies the requirement to run the cached-policy lookup in the same Azure Container Apps compute; Telemetry: AzureAIOpenTelemetryTracer added as callbacks.
Current Microsoft LangGraph integration guidance supports wrapping Foundry ' s FileSearchTool through AgentServiceBaseTool for the primary project-vector-store path and attaching AzureAIOpenTelemetryTracer through LangGraph callbacks for OpenTelemetry traces. The problematic part is the proposed outage fallback. Code Interpreter is a Foundry-hosted built-in tool, so it does not execute in the same Azure Container Apps compute environment as the LangGraph application. That means a CodeInterpreterTool- based fallback cannot satisfy the explicit same-compute requirement. A technically correct design would use a local LangGraph/LangChain callable or tool over cached policy content mounted or stored with the Container Apps workload. Because that local-tool option is absent from the answer set, there is no fully valid listed selection for the fallback row. The updated answer therefore preserves the two valid selections and explicitly identifies the missing valid option rather than endorsing an inconsistent one. The implementation should also preserve clear inputs and outputs around this step so that later agents receive only the information they require. This improves debuggability and keeps token, permission, and state growth under control as the workflow becomes more complex.
Official Microsoft reference: Microsoft Foundry - develop LangChain/LangGraph agents
66. Frage
You have a multi-agent solution in a Microsoft Foundry project. The project connects to an Azure Storage account named stgaudit.
You plan to enable a storage-backed tool for the agent The tool will read and write blobs to stgaudit.
You need to create a role assignment for the agent. The solution must follow the principle of least privilege.
Which role should you use?
Antwort: D
Begründung:
The tool only needs to read and write blob data in the `stgaudit` storage account. Storage Blob Data Contributor is the built-in data-plane role that grants the required blob read/write capabilities without granting unnecessary ownership or broad resource-management authority. Storage Account Contributor and the generic Contributor role operate at the management plane and are wider than necessary for this data-access requirement. Storage Blob Data Owner also exceeds the stated need by including additional control over blob data permissions/ownership. Microsoft ' s Azure Storage RBAC guidance separates data-plane blob roles from management roles and recommends choosing the narrowest role that supports the required operation.
Because the agent must both read and write blob content, D is the least-privilege role among the options. The same configuration should be paired with auditable identity, trace, and evaluation data so reviewers can prove which principal acted, which policy was applied, and why a request was allowed or blocked. That is particularly important for production multi-agent systems with external tools.
Official Microsoft reference: Azure Storage - assign Azure roles for blob data access
67. Frage
You have a Microsoft Foundry multi-agent solution that includes the following agents:
* An orchestration agent
* An external risk-review agent operated by a partner organization
The partner agent runs in a different framework, exposes its own endpoint, and manages long-running review tasks. You need to identify an integration approach for the partner agent. The solution must meet the following requirements:
* Use authenticated and auditable access for structured tools and data
* Support capability discovery and task tracking for the partner agent.
* Prevent coupling the solution to the partner agent implementation.
What should you identify?
Antwort: A
Begründung:
The partner component is an independently hosted agent running a different framework and managing long- running tasks. Agent-to-Agent (A2A) is designed for exactly this inter-agent boundary. It exposes a standards- based endpoint, supports capability discovery through agent cards, and provides task/context identifiers for long-running interactions without requiring the calling solution to know the partner ' s internal implementation. MCP primarily exposes tools and resources to an agent rather than representing a remote autonomous agent with its own task lifecycle. Platform-native subagent orchestration and in-process agent-as- tool composition would also couple the solution to a specific runtime or process boundary. Because the requirement is interoperable agent communication plus capability discovery and task tracking, A2A is the correct integration approach. This choice also keeps the design composable as more agents are introduced.
Clear interfaces, trust boundaries, and state ownership let a team change one domain without forcing unrelated agents to adopt the same permissions, context, or execution model.
Official Microsoft reference: Microsoft Agent Framework - Agent-to-Agent provider
68. Frage
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AI-500 Fragenkatalog: https://www.pass4test.de/AI-500.html