AI-500 試験問題を無料オンラインアクセス

You have a multitenant platform that uses Microsoft Foundry agents. The agents use conversation-based history for active turns, and the platform runs on multiple stateless container instances. Each tenant has a different transcript retention period, and users expect preferences from previous conversations to influence future sessions.
You need to persist cross-session memory after a service conversation is deleted The solution must meet the following requirements:
* Apply tenant-specific retention to transcript exports.
* Keep the compute tier stateless during scale-out.
* Minimize operational complexity
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

Explanation:
Extracted memory facts: Azure Cosmos DB; Cache: Azure Managed Redis with tenant-prefixed keys; Transcript exports: A shared blob container with lifecycle management policies.
The design separates durable memory, distributed cache, and retained transcripts. Azure Cosmos DB is appropriate for durable extracted facts that must survive deletion of a service conversation and remain available across stateless compute instances. Azure Managed Redis provides a shared low-latency cache; tenant-prefixed keys are a standard multitenant pattern that avoids local-process affinity. Transcript exports belong in Blob Storage, where lifecycle management rules can enforce retention and can be filtered by prefixes or tags so different tenant retention policies can coexist in one managed storage design. This combination keeps the compute tier stateless while avoiding unnecessary per-tenant infrastructure. The important security requirement is that every persistence layer also enforce tenant authorization, not just naming conventions. Within the options provided, the stated combination minimizes operational complexity while meeting cross-session persistence and retention needs. 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 Architecture Center - multitenant data and cache patterns

You are designing a multitenant software as a service (SaaS) platform that uses multiple agents. Users will send latency-sensitive inference requests to the platform by using a shared API.
Initially, there will be 20 tenants, and the platform will expand to 200 tenants.
You need to identify the compute component for a production agent runtime. The solution must meet the following requirements:
Isolate workloads for each tenant by using containerization.
Dynamically scale based on demand.
Minimize administrative effort.
What should you use?

• A.Azure Kubernetes Service (AKS)
• B.Microsoft Foundry Agent Service
• C.Azure Container Instances
• D.GPU-enabled Azure virtual machines

You are designing Microsoft Foundry multi-agent solution. The agents will use Agent-to-Agent (A2A) delegation and access separate Azure Storage containers within a lesource group named RG1.
You need to recommend identity components for the design. The solution must meet the following requirements:
* Eliminate stored application secrets.
* Limit the impact of a compromised agent or deployment.
Solution: Use a shared single-tenant application registration for the agents Assign Azure roles at the subscription scope. Does this meet the goal?

• A.No
• B.Yes

You have a Microsoft Foundry claims-assistance solution that includes a primary claims agent and three specialist agents. The primary claims agent invokes the specialist agents.
You need to recommend a coordination approach. The solution must meet the following requirements:
* The primary claims agent must retain ownership of the task and synthesize the final response.
* The specialist agents must have a least-privilege view of the claim for their assigned work.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

Explanation:
Orchestration pattern: Agent-as-tools; Context passed to specialists: Scoped claim data only.
The primary claims agent must retain task ownership and synthesize the final response, which is the defining property of the agents-as-tools pattern. The primary agent decides when to invoke each specialist, receives the specialist ' s result, and remains responsible for the overall conversation. Handoff orchestration would instead transfer control to another agent and therefore weaken the ownership requirement. Microsoft Agent Framework also makes it possible for the outer agent to pass only the arguments needed by the inner agent rather than sharing the entire conversation history automatically. That supports least-privilege context: each specialist receives only the claim fields required for its assigned work. Passing the whole claim or a shared transcript would unnecessarily expose data outside the specialist ' s domain. The selected combination therefore satisfies both control ownership and data minimization. The architecture should still be validated with representative end-to-end tests, but the selected component establishes the correct structural boundary first. Microsoft ' s AI-500 blueprint consistently favors explicit scopes, interfaces, and persistence or identity boundaries over prompt-only conventions.
Official Microsoft reference: Microsoft Agent Framework - Agents as tools

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.

正解：

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