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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Design and implement evaluation and validation strategies
|
| Topic 2: Secure, govern, and deploy multi-agent solutions | 20-25% | - Deploy multi-agent solutions to Azure
|
| Topic 3: Architect multi-agent solutions | 15-20% | - Design logical architecture for multi-agent solutions
|
| Topic 4: Develop multi-agent solutions in Azure | 30-35% | - Implement multi-agent orchestration
|
IT業界での先駆者として、我々CertShikenの目的はIT認定試験に参加する皆様に助けを提供することです。我々のエリートたちは目標を達成するために、昼も夜も努力してMicrosoft試験の数年以来のデータの分析と整理に就職しています。彼らの真面目な態度があって、我々のAI-500対策を利用するお客様のほとんどはAI-500試験に合格できます。
質問 # 61
You have a Microsoft Foundry agent that handles customer support chats. The agent has two tools named get_contract_terms and calculate_refund.
On turns where refunds must be calculated, the agent inconsistently calls the tools.
You need to reliably force refund-estimate turns to call calculate_refund.
Which request configuration should you use?
正解：C
解説：
The requirement is to force one specific tool, `calculate_refund`, whenever the application determines that a refund estimate is required. A named function in `tool_choice` removes ambiguity by directing the model to invoke that exact function. `tool_choice: " auto " ` allows the model to decide whether to call a tool and which tool to use. `tool_choice: " required " ` forces some tool call but does not guarantee that `calculate_refund` is selected when multiple tools are available. A natural-language instruction remains probabilistic and is not equivalent to an API-level enforcement setting. Microsoft ' s function-calling API documents named tool choice as the deterministic way to force a specific function. Therefore A is correct. 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. 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: Azure OpenAI - Function calling and tool choice
質問 # 62
You have a Microsoft Foundry multi-agent solution that includes a orchestrator and agents named Agent1 and Agent2. The solution has the following characteristics:
* The orchestrator routes tasks to both agents.
* The agents invoice Model Context Protocol (MCP)-hosted tools.
* Agent? sends model-generated JSON parameters directly to tool endpoints.
* The agents use a shared managed identity that has contributor access to the resource group that contains the tool-backed Azure resources.
You need to implement a guardrail strategy for tool calls. The solution must meet the following requirements:
* Prevent invalid or out-of-range tool parameters from reaching the tool endpoints.
* Scope each agent ' s permissions to its assigned tools
* Limit Agent? to refund-processing tools only.
* Log tool invocations for audit purposes.
What should you do?
正解：D
解説：
The solution needs deterministic validation before model-generated tool arguments reach MCP endpoints, per- agent authorization boundaries, tool scoping, and audit logging. Validating typed payload schemas at the tool boundary prevents malformed or out-of-range arguments before execution, which is stronger than validating outputs after a tool has already run. Per-agent managed identities and scoped RBAC roles reduce lateral movement and contain the impact of a compromised agent. A per-agent tool-access policy also ensures the refund-focused agent cannot invoke unrelated tools. Finally, Azure Monitor/trace logging provides an auditable record of tool invocations. A2A capability cards describe remote-agent capabilities rather than validate tool arguments. System instructions are not an authorization boundary, and shared Contributor access leaves an excessive blast radius. Option D is therefore the only answer that satisfies all four requirements at enforceable platform boundaries. 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: AI-500 Study Guide - tool scopes, permission boundaries, and auditing
質問 # 63
You have a LangGraph multi-agent workflow that uses Azure Cosmos DB as a checkpointer.
You plan to change the pruning rules for the running message list before the next release You need to implement evaluations tor memory. The solution must meet the following requirements:
* Verify that claim-related state values remain correct at transition points.
* Verify that conversation context is preserved after an interruption.
* Identify the source of any loss of continuity during a run
Which three actions should you perform Each correct answer presents part of the solution NOTE: Each correct selection is worth one point.
正解：B、C、D
解説：
Memory evaluation must verify persistence across interruption, correctness of checkpointed state, and enough telemetry to locate where continuity was lost. Running a scripted multi-turn case with one stable `thread_id` before and after a restart directly tests whether the Cosmos-backed LangGraph checkpointer restores the same conversation/workflow. Comparing persisted checkpoint documents with expected state values at each handoff verifies that claim-related state has not been corrupted by the new pruning rules. Correlating the evaluation run with trace spans then provides the diagnostic path to identify which agent transition or state- management operation caused a failure. Changing the thread ID each turn would intentionally create separate state scopes, and removing durable artifacts would defeat the persistence test. Tool-call arguments alone do not prove workflow-memory continuity. Therefore D, E, and F are the complementary actions required. The evaluation should also preserve correlation identifiers and version information where possible so a failed score can be traced back to the exact agent, model, tool call, or retrieval step that produced it. This turns the metric into an actionable diagnostic rather than only a dashboard number.
Official Microsoft reference: Azure Cosmos DB integrations for LangGraph and agent state
質問 # 64
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?
正解：B
解説：
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
質問 # 65
You have a Microsoft Foundry project that contains a multi-agent customer support solution. The solution includes a final response agent.
You need to provide reviewers with the ability to assess agent responses in the preview web app and record a completion score for each agent response. The solution must follow the principle of least privilege.
Which roles should you assign to the reviewers?
正解：D
解説：
Microsoft ' s human-evaluation guidance for the Foundry preview web app requires reviewers to have Foundry User permissions on the project plus Reader access on the Foundry account/resource. That combination gives the reviewer enough project-level access to open the agent experience and record evaluation feedback while avoiding project-management or account-management privileges. Application Insights Reader alone only grants telemetry visibility and does not provide the Foundry interaction permissions needed to score responses. Foundry User alone does not satisfy the documented account-level visibility requirement for this preview experience. Foundry Project Manager would be broader than necessary and violate the least-privilege objective. Therefore C is the documented reviewer role combination. 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. 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.
Official Microsoft reference: Microsoft Foundry - Human evaluation
質問 # 66
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