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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, govern, and deploy multi-agent solutions | 20–25% | - Apply security and compliance
|
| Topic 2: Evaluate, optimize, and monitor multi-agent solutions | 20–25% | - Implement observability
|
| Topic 3: Architect multi-agent solutions | 15–20% | - Design logical architecture for multi-agent systems
|
| Topic 4: Develop multi-agent solutions in Azure | 30–35% | - Implement agents using Azure AI services
|
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NEW QUESTION # 43
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.
Answer:
Explanation:
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
NEW QUESTION # 44
You need to implement a logging solution for claim Approval.
What should you use for each process? To answer, drag the appropriate resources to the correct processes.
Each resource may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Log the start/completion of each specialist run and inspect the final output with Agent run middleware; record each customer-refund-tool call, including function name and arguments, with Function calling middleware.
The two logging requirements occur at different lifecycle boundaries. Agent run middleware surrounds an agent invocation, so it is the appropriate place to capture run start, run completion, exceptions, and the final agent result. Function calling middleware surrounds tool/function execution and can inspect the selected function, arguments, result, and failures. That makes it the correct boundary for an audit record of each customer-refund-tool invocation. Foundry tracing can provide broader distributed observability, but the question asks for reusable Microsoft Agent Framework middleware at the exact execution points. A knowledge store or memory store does not provide execution interception. Separating the two concerns also supports a cleaner audit model: agent-level middleware records specialist behavior, while function-level middleware records tool use. This aligns with Microsoft ' s middleware model, where cross-cutting concerns such as logging, validation, and exception handling can be implemented once and applied consistently rather than duplicated inside every agent or tool.
Official Microsoft reference: Microsoft Agent Framework - Defining middleware
NEW QUESTION # 45
You need to recommend an end-to-end release lifecycle for claim Approval. The solution must meet the technical requirements.
What should you recommend for each requirement? To answer, drag the appropriate recommendations to the correct requirements. Each recommendation may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Provision Test, Acceptance, and Production by using a Bicep file applied through the CI/CD pipeline in Azure Pipelines; block promotion when quality falls below the threshold by using Foundry Agent Evaluator as a pipeline release gate.
The release lifecycle must satisfy two independent requirements: repeatable infrastructure deployment across DTAP environments and an automated quality gate before promotion. Bicep is Azure ' s infrastructure-as- code language, and Microsoft documents deploying Bicep through Azure Pipelines so the same declarative resources can be promoted consistently across subscriptions. For the promotion decision, an automated Foundry evaluation is the appropriate control because response quality is an AI-system property that should be measured against a defined threshold before the next stage is released. A manual portal deployment would weaken repeatability, while a deployment-only gate would not measure model or agent behavior. The important design point is that infrastructure provisioning and AI quality validation are separate controls in the same pipeline: Bicep establishes environment parity, and the evaluation gate prevents a technically deployable but behaviorally degraded agent version from advancing. From a security and governance perspective, the control should be enforced at the narrowest platform boundary that can deterministically block or constrain the action. Relying only on prompt text is weaker because the model can still be induced to behave unexpectedly.
Official Microsoft reference: Deploy Bicep files with Azure Pipelines; AI-500 Study Guide
NEW QUESTION # 46
You have a Microsoft Foundry project that contains an incident triage agent.
You have a Model Context Protocol (MCP) server registered in the organizational tool catalog. The MCP server exposes two tools named docs_search and deployment_delete.
You need to ensure that the agent can only invoke docs_search.
What should you configure?
Answer: D
Explanation:
The restriction belongs in the agent ' s MCP tool configuration because Microsoft Foundry supports an
`allowed_tools` allowlist that controls which tools discovered from an MCP server are exposed to the agent.
Configuring the allowlist to include only `docs_search` makes `deployment_delete` unavailable for model selection. This is stronger than adding a sentence to the agent instructions because instructions influence behavior but do not remove a dangerous tool from the callable surface. Project details describe resources rather than per-agent tool exposure, and a transient run setting is not the appropriate persistent configuration boundary for the registered MCP integration. Therefore C, the agent tool configuration, is the correct answer.
Least privilege remains the governing principle: grant only the identity, data, tool, or deployment access required for the specific operation. The selected answer preserves that boundary while still allowing the workflow to satisfy its functional requirement. From a security and governance perspective, the control should be enforced at the narrowest platform boundary that can deterministically block or constrain the action.
Relying only on prompt text is weaker because the model can still be induced to behave unexpectedly.
Official Microsoft reference: Microsoft Foundry agents - Model Context Protocol tools
NEW QUESTION # 47
You have a Microsoft Agent Framework solution for customer support that includes the following agents:
* A triage agent
* A refund specialist agent that runs in the same process as the triage agent
* A compliance review agent that is exposed by a partner team as a remote service You need to identify which integration components the triage agent will use to coordinate interactions with the other agents.
What should you identify for each agent? To answer, drag the appropriate components to the correct agents Each component may be used once, more than once, or not at all You may need to drag the split bar between panes or scroll to view content NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Refund specialist agent: Agent-as-tools; Compliance review agent: Agent-to-Agent (A2A) protocol.
The refund specialist runs in the same process as the triage agent, so exposing it as an agent-as-tool is the lightweight composition pattern. The outer triage agent can decide when to invoke the specialist while retaining overall task ownership. The compliance reviewer is different: it is a remote service owned by a partner team. Agent-to-Agent (A2A) is the interoperable protocol intended for independently hosted agents that may use a different runtime or framework. It supports capability discovery and remote task interaction without coupling the caller to the partner ' s implementation. Using A2A for the in-process specialist would add unnecessary network/protocol complexity, while treating the partner agent as an in-process tool would not match the deployment boundary. The two selections therefore reflect Microsoft ' s distinction between local agent composition and remote agent interoperability. 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 Agent Framework - agents as tools and A2A
NEW QUESTION # 48
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