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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architect multi-agent solutions | 15-20% | - Design logical architecture for multi-agent solutions
|
| Topic 2: Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Design and implement evaluation and validation strategies
|
| Topic 3: Secure, govern, and deploy multi-agent solutions | 20-25% | - Design and implement security for multi-agent solutions
|
| Topic 4: Develop multi-agent solutions in Azure | 30-35% | - Design and implement advanced prompt engineering strategies
|
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質問 # 56
You are designing an enterprise automation solution that has a web portal for users and the following types of workflows:
* Routine workflows that use prompt-defined agents, approved knowledge stores, and HTTPS tools
* Modernization workflows that use custom Microsoft Agent Framework code packaged as container images with predefined handoffs and task states You need to recommend Azure services that meets the following requirements:
* The routine workflows must run agents in a fully managed environment.
* The user portal must be deployed as a managed platform as a service (PaaS) web app
* The modernization workflows must run agents in serverless containers that support automatic scaling What should you recommend for each requirement? To answer, drag the appropriate services to the correct components. Each service 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.
正解：
解説：
Explanation:
Routine workflows: Microsoft Foundry Agent Service; User portal: Azure App Service; Modernization workflows: Azure Container Apps.
The three workload types map to three different managed Azure services. Prompt-defined agents that use approved knowledge and HTTPS tools fit Microsoft Foundry Agent Service because it provides a managed agent runtime and hosted agent capabilities without requiring the team to operate the underlying orchestration infrastructure. The user portal is a conventional managed web application, making Azure App Service the direct PaaS fit. The modernization workflow is custom Microsoft Agent Framework code packaged as container images and therefore needs a serverless container platform; Azure Container Apps supplies managed container execution and automatic scaling while preserving control of the custom application image.
Logic Apps targets integration workflows rather than arbitrary Agent Framework containers, and Copilot Studio is not the requested general-purpose container runtime. The answer therefore selects the managed service that matches each component ' s execution model. 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 Agent Service overview
質問 # 57
Solution: Use separate a managed identity for each agent and environment Assign Azure roles at the resource level. Does this meet the goal?
正解：A
解説：
Separate managed identities for each agent and environment remove the need to store application secrets and create independent authorization boundaries. Assigning Azure roles at the resource level further limits each identity to only the Storage resource it requires. This sharply reduces lateral movement compared with a shared application principal or subscription-wide role. Microsoft Foundry and Azure identity guidance consistently recommend identity-based authentication, distinct identities when permissions differ, and the narrowest practical RBAC scope. In current Foundry deployments, the exact identity object may be represented through Microsoft Entra agent identity or a federated managed identity relationship, but the architectural principle in the option is correct. Therefore the solution meets both stated goals and the answer is A, Yes. 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 agent identity
質問 # 58
You have a Microsoft Foundry multi-agent solution. The solution includes a parent agent that can call an Azure logic app and delegate to two subagents.
You need to implement a review process for flagged interactions. The solution must meet the following requirements;
* Identify requests that call third-party services.
* Moderate the prompts, steps, and tool calls.
* Include a governance review.
What should you do?
正解：B
解説：
The review process must cover the full sensitive interaction, including third-party calls, prompts, intermediate steps, and tool actions, and it must feed a governance review process. A centralized sensitive-use intake combined with guardrails and tracing provides the required evidence, while human reviewers must be able to approve, edit, or reject flagged interactions rather than reviewing only the final message. Content Safety alone does not provide full process governance over tool use. Routing only subagent findings or requiring approval only for the final response misses earlier third-party actions. CI/CD evaluation is important for release quality but does not control individual flagged production interactions. The source duplicated the label C for the final option; that final option should be labeled D. With that label correction, D is the best answer. 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 - governance, guardrails, tracing, and HITL
質問 # 59
You have a Microsoft Foundry multi-agent customer support solution that retrieves grounding data from a shared vector index. The indexed corpus contains product runbooks in Markdown and support articles in HTML Both document types use a consistent hierarchical markup.
You discover that current fixed-size token chunking creates chunks that cross section boundaries.
You need to recommend a chunking approach for the ingestion pipeline. The solution must preserve existing document structure boundaries and minimize custom chunking code.
What should you recommend?
正解：A
解説：
The corpus already contains reliable document hierarchy in Markdown and HTML, so the ingestion pipeline should preserve those author-defined boundaries rather than infer new ones from token counts or topic shifts.
Format-specific header splitters can divide Markdown by heading levels and HTML by structural headers, producing chunks that align with meaningful sections. This directly solves the current problem of fixed-size token chunks crossing section boundaries and requires less custom logic than building a semantic topic-shift chunker. Recursive character splitting can be configured with structure-aware separators, but it remains a more generic fallback when format-specific structure is already available. Microsoft Azure AI Search guidance recommends exploiting document structure such as headings when chunking. Therefore B is the most direct and maintainable approach. 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 AI Search - structure-aware chunking and Markdown indexing
質問 # 60
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.
正解：
解説：
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
質問 # 61
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