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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluate, optimize, and monitor multi-agent solutions | 20โ25% | - Implement observability
|
| Topic 2: Develop multi-agent solutions in Azure | 30โ35% | - Implement agents using Azure AI services
|
| Topic 3: Secure, govern, and deploy multi-agent solutions | 20โ25% | - Deploy and maintain solutions
|
| Topic 4: Architect multi-agent solutions | 15โ20% | - Design workflow and tool integration
|
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NEW QUESTION # 68
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 # 69
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 # 70
You need to recommend a Microsoft Foundry multi-agent solution that has the following domain-specific requirements:
* Agent responses must never include the names of two specific sanctioned companies.
* Agent responses must NOT expose customer account numbers
Which guardrail strategy should you include in the recommendation?
Answer: C
Explanation:
Both stated restrictions concern content that must not appear in the agent ' s final output. A custom blocklist should therefore be applied to agent output so the two sanctioned company names are detected before a response is returned. The customer-account requirement likewise belongs at the output boundary, where PII
/sensitive-data detection can identify protected identifiers in generated content. Applying the controls only to user input would not stop the model from generating the prohibited names or identifiers itself. Task Adherence focuses on whether agent behavior stays within assigned procedures and is not a substitute for explicit output filtering. Microsoft Foundry guardrails are designed to attach controls to the intervention point where the relevant risk occurs. Because the risk is disclosure in the response, option C is the configuration that places both controls at the correct boundary. 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: Microsoft Foundry guardrails - intervention points
NEW QUESTION # 71
You are designing a Microsoft Foundry multi-agent solution. The solution includes a triage agent that receives a request and passes the request to one of three specialist agents.
Only one agent will be active at a time. The active agent must be able to transfer control to a better-suited agent based on context.
Which orchestration pattern should you recommend for the planned solution?
Answer: B
Explanation:
Only one agent should be active at a time, and the active agent must be able to transfer control dynamically when another specialist is better suited. Microsoft Agent Framework defines handoff orchestration for precisely that situation: task and conversational ownership move from one agent to another according to context. Concurrent orchestration would activate multiple specialists at the same time, contradicting the requirement. Sequential orchestration follows a predetermined order rather than allowing contextual transfer.
Group chat keeps multiple agents participating in a shared conversation rather than modeling single-owner control. The decisive requirement is therefore transfer of ownership, not merely calling another agent for a subtask. That makes B, handoff, the correct orchestration pattern. In production, add telemetry and regression tests around this behavior so changes to prompts, models, tools, or orchestration do not silently alter the intended contract. The selected approach is the one that best matches the platform ' s native execution semantics.
Official Microsoft reference: Microsoft Agent Framework - Handoff orchestration
NEW QUESTION # 72
You need to design a solution to resolve the Scheduling agent issue.
What should you include in the design?
Answer: B
Explanation:
The Scheduling agent ' s MCP server fails transiently during peak load. Microsoft Azure reliability guidance recommends retries with exponential backoff for transient faults so that callers do not immediately repeat requests at the same rate and worsen a temporarily overloaded dependency. Immediate retries can create a retry storm, especially when many agent requests encounter the same service condition. Routing the tool call through a different agent does not remove the underlying MCP dependency and would only add coupling. A well-designed retry policy should normally include a bounded retry count, increasing delay, jitter where appropriate, and respect for server-provided retry guidance when available. The scenario asks which design element directly addresses the timeouts, and exponential backoff is the established resilience pattern for transient service saturation. Therefore A is the correct choice. 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: Azure Well-Architected Framework - Handle transient faults
NEW QUESTION # 73
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