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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, govern, and deploy multi-agent solutions | 20–25% | - Apply security and compliance
|
| Topic 2: Architect multi-agent solutions | 15–20% | - Design workflow and tool integration
|
| Topic 3: Evaluate, optimize, and monitor multi-agent solutions | 20–25% | - Assess performance and reliability
|
| Topic 4: Develop multi-agent solutions in Azure | 30–35% | - Manage state and memory
|
>> AI-500 Prüfungsmaterialien <<
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11. Frage
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?
Antwort: A
Begründung:
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
12. Frage
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?
Antwort: C
Begründung:
Microsoft Foundry Agent Service is the managed option that best satisfies containerized production agent execution, dynamic scaling, and low administrative overhead. Hosted agents run in Foundry-managed container compute and scale according to workload demand while the platform manages much of the runtime, endpoint, identity, and operational plumbing. AKS could provide strong tenant/workload isolation and autoscaling, but it also requires Kubernetes cluster operations, policy, upgrades, node pools, and scaling configuration, which conflicts with the requirement to minimize administration as the tenant count grows.
Azure Container Instances is simpler but lacks the same managed autoscaling model, and GPU virtual machines create the highest infrastructure burden. Therefore C is the strongest fit for a SaaS platform that wants production agent compute without owning the orchestration platform. From an architecture perspective, the selection also creates explicit ownership and boundaries that can be tested independently. That matters in multi-agent systems because implicit sharing or loosely defined authority often becomes the source of cross- agent coupling, security drift, and difficult incident diagnosis.
Official Microsoft reference: Microsoft Foundry Agent Service overview
13. Frage
You have a single-agent customer support copilot built by using Microsoft Foundry. The application that hosts the copilot runs on multiple stateless instances, and rolling upgrades restart the instances.
[Architect multi-agent solutions]
Customer investigations can last for several days. A user can have multiple unrelated investigations, and stable user preferences, such as a preferred contact method, must be available across sessions and devices Compliance reporting requires a one-year, queryable audit trail of thread messages and session metadata.
During long investigations, the model context window regularly approaches its limits. Temporary tool traces do NOT affect final outcomes after the run completes.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Yes / No / Yes
Persisting thread messages and session metadata in Azure Cosmos DB can satisfy the requirement for a durable, queryable audit trail when the appropriate retention and indexing policies are configured. Storing thread identifiers only in per-instance memory cannot survive rolling restarts and cannot support consistent routing across multiple stateless instances, so that statement is false. Separating unrelated investigations into task-scoped threads prevents one case from consuming the context budget of another. Removing temporary tool traces after the run when they no longer influence the outcome is also a valid compaction strategy for reducing context pressure. Current Agent Framework terminology commonly refers to this as compaction or tool-result eviction rather than "importance-weighted pruning," but the architectural effect described is sound.
Therefore the correct sequence is Yes, No, Yes. 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 Foundry Agent Service - standard setup and durable state
14. 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
15. Frage
You have a Microsoft Foundry project that includes four independent analysis agents. Each agent invocation consumes 500 tokens per minute (TPM) from a Foundry deployment that has a TPM rate limit of 1,000.
After each agent completes, it writes 200 small records to Microsoft Dataverse. Running multiple agents simultaneously causes write bursts that result in HTTP 429 (Too Many Requests) responses.
You need to reduce the end-to-end task duration, while preventing provider and platform throttling. The solution must meet the following requirements:
* Keep as much agent parallelism as the TPM rate limit permits.
* Handle Dataverse throttling without sending premature retries.
How should you configure the orchestration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Maximum agent concurrency: Two agents; Dataverse retry behavior: Respect the Retry-After duration returned by Dataverse.
Each analysis agent consumes 500 TPM and the deployment permits 1,000 TPM, so at most two agents can run simultaneously without exceeding the stated model limit. Running only one wastes available parallelism; running three or four violates the limit. The separate Dataverse problem is HTTP 429 service-protection throttling. Microsoft Dataverse returns a `Retry-After` duration that tells clients how long to wait before retrying. Respecting that value prevents premature retries from worsening the overload condition. A fixed retry interval or immediate retry ignores the server ' s current capacity signal. The orchestration should therefore cap model-side concurrency at two and, after each agent finishes, pace Dataverse writes according to the returned retry guidance. This combination minimizes task duration while honoring both provider and platform throttling constraints. 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: Dataverse service protection API limits
16. Frage
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