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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architect multi-agent solutions | 15โ20% | - Design workflow and tool integration
|
| Topic 2: Secure, govern, and deploy multi-agent solutions | 20โ25% | - Apply security and compliance
|
| Topic 3: Evaluate, optimize, and monitor multi-agent solutions | 20โ25% | - Implement observability
|
| Topic 4: Develop multi-agent solutions in Azure | 30โ35% | - Implement agents using Azure AI services
|
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NEW QUESTION # 17
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?
Answer: C
Explanation:
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
NEW QUESTION # 18
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?
Answer: A
Explanation:
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
NEW QUESTION # 19
You have a Microsoft Foundry project that contains a coordinator agent for claims processing.
You have an existing fraud analysis agent hosted outside of Foundry. The agent exposes a standards-based HTTPS endpoint, publishes the supported s kills and media types, and requires authenticated requests.
You need to configure the coordinator agent to invoke the fraud analysis agent. The solution must meet the following requirements:
Use the interoperable invocation interface.
Use the published capability discovery contract.
Reuse stored authentication across versions of the coordinator agent.
How should you configure the integration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Integration mechanism: Agent-to-Agent (A2A) tool; Capability discovery contract: Agent cards; Credential and endpoint configuration: A Foundry project A2A connection.
The remote fraud service is an autonomous agent that exposes a standards-based HTTPS endpoint and publishes its skills and media types, which matches the Agent-to-Agent protocol rather than a conventional tool API. A2A uses an agent card as the capability-discovery contract, allowing the coordinator to discover supported skills, formats, and invocation details without coupling to the remote framework ' s implementation.
Microsoft Foundry project A2A connections store the remote endpoint and authentication configuration so credentials can be reused across coordinator-agent versions instead of being embedded separately in every version. Together, the A2A tool, agent card, and project connection satisfy interoperable invocation, capability discovery, and reusable authentication. This is the supported Microsoft architecture for calling an external A2A-compatible agent from a Foundry coordinator. The configuration should be kept in the project connection rather than prompt text so authentication remains centrally managed, auditable, and reusable as the coordinator evolves. This also avoids duplicating sensitive connection settings across agent versions.
Official Microsoft reference: Microsoft Foundry - enable Agent-to-Agent endpoint and authentication
NEW QUESTION # 20
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?
Answer: B
Explanation:
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
NEW QUESTION # 21
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 resource 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 delegated user permissions for agent actions. Assign Azure roles by using a shared security group. Does this meet the goal?
Answer: A
Explanation:
The proposed solution uses delegated user permissions for agent actions and assigns Azure roles through one shared security group. Even if it avoids storing an application secret, it does not create strong per-agent authorization boundaries. Sharing downstream permissions through one group can allow a compromised agent to inherit access intended for other agents, which conflicts with the requirement to limit blast radius.
Microsoft Foundry identity guidance recommends distinct logical agent/workload identities and narrowly scoped role assignments when agents have different resource responsibilities or audit requirements. Delegated user access is appropriate when an operation genuinely needs the signed-in user ' s authorization context, not as a general service-to-service isolation model for autonomous A2A workers. Therefore the proposed design does not meet both goals, and B, No, remains correct. 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 agent identity
NEW QUESTION # 22
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