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| Section | Weight | Objectives |
|---|---|---|
| Evaluate, optimize, and monitor multi-agent solutions | 20–25% | - Assess performance and reliability
|
| Develop multi-agent solutions in Azure | 30–35% | - Implement agents using Azure AI services
|
| Architect multi-agent solutions | 15–20% | - Design workflow and tool integration
|
| Secure, govern, and deploy multi-agent solutions | 20–25% | - Deploy and maintain solutions
|
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NEW QUESTION # 23
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.
Answer:
Explanation:
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
NEW QUESTION # 24
You have a Microsoft Foundry multi-agent solution.
A developer publishes a new version of a specialist agent. Once the agent goes live in production, the solution starts mishandling requests.
You need to restore the previous behavior as quickly as possible
What is the fastest way to roll back the agent?
Answer: C
Explanation:
The fastest safe rollback is to route the stable endpoint back to the previous known-good immutable agent version. Current Foundry lifecycle guidance supports versioned agents and endpoint/version selection so production traffic can be redirected without rebuilding the agent from scratch. Deleting the newly published version is a destructive cleanup action and is not the preferred rollback mechanism because it removes an artifact that may be needed for diagnosis. Creating a new agent changes the lifecycle identity and takes longer, while a complete redeployment is unnecessary if the earlier version already exists. Option C is therefore correct when interpreted as changing the endpoint ' s active-version or version-selector configuration to the previous version while keeping the endpoint URL stable. 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 agents - development lifecycle and versioning
NEW QUESTION # 25
You have a Microsoft Foundry Agent Sen/ice 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 set scope for each memory search tool to
{{Suserld}} Does this meet the goal?
Answer: A
Explanation:
A dedicated memory store for each agent establishes agent-domain isolation. Setting each memory search tool
' s scope to the current user identity establishes the second isolation dimension: users within the same agent domain receive separate logical memory collections. Microsoft Foundry Memory documents `scope` as the partitioning mechanism and specifically supports `{{$userId}}` for automatic per-user isolation. Memory associated with one scope can be deleted without removing memories stored under other scopes, which satisfies the deletion requirement. The source text renders the token imperfectly as `{{Suserld}}`; the current documented syntax is `{{$userId}}`. Interpreting the question as the documented user-scope token, the design satisfies domain isolation, user isolation, and per-user deletion. Therefore A, Yes, 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.
Official Microsoft reference: Create and use memory in Foundry Agent Service
NEW QUESTION # 26
You have a multi-agent solution in Microsoft Foundry. Every request begins with the same 1,800-token instruction block and Model Context Protocol (MCP) tool definitions, and then appends a unique user message.
Input-token costs and Time to First Token (TTFT) increase during peak hours.
You need to reduce the input-token costs and TTFT for requests that share common instructions and tool definitions. The solution must meet the following requirements:
Reuse cached work only when the common prefix matches exactly.
Generate a new completion for each user request.
Minimize application changes.
Which type of caching should you use?
Answer: C
Explanation:
Prompt caching is designed for requests that share a long, identical prefix but still require a new completion for the unique user input. Azure OpenAI prompt caching reuses computation for matching prompt prefixes, reducing the effective input cost and improving Time to First Token for supported models. The stable 1,800- token instruction and MCP tool-definition block is large enough to benefit from this mechanism, and the unique user message can remain at the end so each request still generates a fresh response. Response caching would reuse an old completion, which violates the requirement. Semantic caching matches similar meaning rather than exact prompt prefixes, and retrieval caching applies to knowledge retrieval rather than model prompt processing. Because prompt caching is handled automatically for supported deployments, it also minimizes application changes. Therefore D is correct. 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: Azure OpenAI - Prompt caching
NEW QUESTION # 27
You have a Microsoft Foundry multi-agent solution that includes the following agents:
* An orchestrator agent
* A supplier worker agent that runs the APIs of external suppliers
* A finance worker agent that has confidential enterprise resource planning {ERP) access You need to implement resource access boundaries that meet the following requirements:
* Limit the blast radius if a worker agent is compromised.
* Allow each agent to access only its required downstream resources.
What should you configure? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Identity structure: Separate blueprints for the orchestrator agent and each worker group; Permission assignment: Assign role-specific downstream permissions to each agent identity.
The supplier and finance workers operate in different trust domains: one reaches external supplier APIs while the other has confidential ERP access. Microsoft Entra Agent ID guidance recommends separating blueprint
/identity trust boundaries when compromise of one agent must not expose unrelated credentials or permissions. Each logical agent identity should then receive only the downstream roles required for its own function. This creates clear audit attribution and limits lateral movement. Giving all workers the same role or routing every privileged operation through an overly powerful orchestrator would expand the blast radius.
Creating an identity for every runtime replica is unnecessary when replicas represent the same logical agent role. The correct structure therefore separates the orchestrator and worker trust domains and assigns role- specific permissions to each identity rather than sharing a common authorization envelope. 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 Entra Agent ID - plan agent identity architecture
NEW QUESTION # 28
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