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| Section | Weight | Objectives |
|---|---|---|
| Architect multi-agent solutions | 15-20% | - Design logical architecture for multi-agent solutions
|
| Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Implement observability and monitoring
|
| Secure, govern, and deploy multi-agent solutions | 20-25% | - Deploy multi-agent solutions to Azure
|
| Develop multi-agent solutions in Azure | 30-35% | - Design and implement advanced prompt engineering strategies
|
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NEW QUESTION # 25
You need to recommend a knowledge integration design for a Microsoft Foundry multi-agent solution. The agents answer questions by using shared documentation. The solution must meet the following requirements:
* Updates must be available from a single maintained knowledge layer.
* Retrieval responses must include citations and query details
* Content must support natural-language queries.
Users will ask the agents complex conversational questions. The questions will include follow-up context and terminology that does NOT always match the wording in the documentation.
Which knowledge type should you recommend?
Answer: A
Explanation:
Azure AI Search is the best fit for a centrally maintained documentation layer that must support natural- language retrieval, citations, and query details. Azure AI Search can combine keyword, vector, hybrid, and semantic retrieval so user terminology does not need to match the source wording exactly. It also supports agentic retrieval/knowledge-base patterns that decompose complex conversational questions and return references suitable for grounded citations. A raw File source does not provide the same managed retrieval and ranking capabilities. Work IQ is oriented toward Microsoft 365 work context such as people, mail, meetings, and files, while Fabric IQ focuses on semantic business data in Fabric/OneLake. For a shared documentation corpus with conversational follow-ups and terminology variation, an Azure AI Search index provides the required maintained retrieval layer and observability into the query process. 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 - agentic retrieval
NEW QUESTION # 26
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?
Answer: B
NEW QUESTION # 27
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 # 28
You have a Microsoft Foundry agent named Agent1.
You open Agent1 in the playground and update the instructions.
You need to run a full evaluation against the updated instructions. The solution must meet the following requirements:
* Test the changes by using a synthetic dataset.
* Ensure that the changes are available only for the development team that has access to Agent1.
What should you do first?
Answer: C
Explanation:
The instructions changed in the playground, but a full evaluation needs a stable, versioned agent definition.
Saving the changes as a new version creates an immutable snapshot that can be evaluated against a synthetic dataset while remaining inside the project for the development team. Publishing is a later lifecycle action that makes a version available to broader consumers or production endpoints and is not required for a private development evaluation. Previewing exercises the current playground configuration interactively but does not establish the versioned target needed for repeatable evaluation. Creating an evaluation before saving the new version risks evaluating the previous configuration instead of the intended update. Microsoft Foundry ' s development lifecycle separates edit, save/version, evaluate, and publish stages, so A is the correct first action. A robust evaluation program separates process metrics from final-response metrics. The selected answer measures the layer where the stated failure actually occurs, which is essential for deciding whether to change retrieval, orchestration, prompt behavior, or the final generator.
Official Microsoft reference: Microsoft Foundry agents - development lifecycle
NEW QUESTION # 29
You have a Microsoft Foundry Agent Service solution that includes two agents.
You need to configure memory for the agents. The solution must meet the following requirements:
* Isolate the memory between end users
* Isolate the memory between the agent domains.
* Support the deletion of one user ' s memory without deleting other users ' memory.
Solution: You create a dedicated memory store for each agent and configure a static agent scope value for each memory search tool.
Does this meet the goal?
Answer: A
Explanation:
A separate memory store for each agent correctly isolates the two agent domains, but a static scope shared by every user inside each store does not isolate end users. Multiple users ' memories would be written and searched within the same logical scope. Microsoft Foundry Memory explicitly recommends a user-derived scope such as `{{$userId}}` when per-user isolation is required. Scope-based deletion is also how one user ' s memory can be removed without deleting other users ' memories. With a single static scope, that deletion boundary does not exist. The proposed design therefore solves only the domain part of the problem and fails both user isolation and safe per-user deletion. Since all requirements must be satisfied, the correct answer is B, No. 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: Create and use memory in Foundry Agent Service
NEW QUESTION # 30
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