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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architect multi-agent solutions | 15–20% | - Design workflow and tool integration
|
| Topic 2: Develop multi-agent solutions in Azure | 30–35% | - Implement agents using Azure AI services
|
| Topic 3: Secure, govern, and deploy multi-agent solutions | 20–25% | - Deploy and maintain solutions
|
| Topic 4: Evaluate, optimize, and monitor multi-agent solutions | 20–25% | - Assess performance and reliability
|
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NEW QUESTION # 67
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 one memory store per end user and configure both agents to use each user ' s memory store with a static scope value.
Does this meet the goal?
Answer: A
Explanation:
Creating one memory store for each end user separates users, but allowing both agents to use that user ' s store with the same static scope does not isolate the two agent domains. Memories produced by one agent can occupy the same logical collection as memories produced by the other agent. Microsoft Foundry Memory uses the `scope` parameter to partition a store, so a design that needs both user and domain isolation must preserve both dimensions, commonly through separate agent stores plus per-user scope. The per-user deletion requirement can also be handled more efficiently by deleting a user ' s scope rather than operating a separate store for every user. Because the proposed design fails agent-domain separation, it does not meet all requirements. Therefore B, No, is correct. 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: Create and use memory in Foundry Agent Service
NEW QUESTION # 68
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: B
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 # 69
You have a Microsoft Foundry helpdesk triage agent. Employees sign in to the agent by using Microsoft Entra. The agent can invoke the following tools:
* A ticket search tool that enforces the existing per employee authorization model
* A knowledge article tool that writes to a separate production article repository You need to recommend an identity-based access configuration for the following execution contexts:
* Ensure that interactive ticket searches enforce per employee authorization.
* Constrain approved article updates to the production article repository The solution must meet the following requirements:
* Prevent the use of embedded secrets.
* Follow the principle of least privilege
Which access configurations should you recommend? To answer, drag the appropriate configurations to the correct execution contexts. Each configuration 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:
Interactive ticket lookup: Delegated permissions from the signed-in employee; Approved article updates: A managed identity scoped to write production article records.
Ticket search must preserve the existing per-employee authorization model, so the downstream operation should execute in the signed-in employee ' s delegated identity context. That allows the ticket service to enforce the same user-level permissions it already uses. The knowledge-article update is an application- controlled write to one production repository, so a managed workload identity with only the required write permission is the least-privilege choice. Microsoft identity guidance distinguishes delegated/on-behalf-of access for user-context operations from managed or agent identities for service-to-service work. Both approaches also eliminate embedded secrets when configured with Microsoft Entra authentication. A broad Contributor assignment or shared API key would unnecessarily expand the blast radius and weaken audit attribution. Therefore the mixed model in the answer is intentional: delegated permissions for per-user reads, and a narrowly scoped managed identity for controlled production writes. 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
NEW QUESTION # 70
You need to recommend a memory retrieval strategy to support the planned changes for claim Approval.
What should you recommend? To answer, drag the appropriate resources to the correct requirements. Each resource 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:
Prior claims: memory-store-490; Contact preferences: memory-store-490.
Both requirements describe information that should persist across conversations for the same customer:
previous claim information and stable contact preferences. Microsoft Foundry Memory is intended for durable, cross-session facts and summaries that agents can retrieve later, while a knowledge base is for shared reference content rather than user-specific history. The refund-processing and customer-refund tools are operational interfaces, not persistence layers. Using the existing memory store for both categories therefore matches the scenario ' s design objective. In production, the memory search tool should also be scoped to the end user so one customer ' s memory cannot be retrieved for another customer. Microsoft documents scope- based isolation for memory stores and supports a user-derived scope such as `{{$userId}}`. The key distinction is that the same memory store can hold multiple kinds of durable customer memory as long as retrieval is correctly scoped; there is no need to create separate tools or knowledge indexes for these two user- specific memory categories.
Official Microsoft reference: Create and use memory in Foundry Agent Service
NEW QUESTION # 71
You have a Microsoft Foundry multi-agent solution that uses Microsoft Agent Framework and LangGraph.
Telemetry from the workload is sent to Application Insights.
You need to implement observability components to meet the following requirements:
* Support the investigation of user interactions across agents, models, tools, functions, and API boundaries without relying on verbose production logs.
* Produce recurring and comparable measurements of deployed response quality and safety over time.
What should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Runtime instrumentation: OpenTelemetry distributed tracing with correlation IDs; Post-deployment assessment: A scheduled evaluation that uses test datasets.
Distributed tracing is the correct runtime mechanism because the objective is to reconstruct a user interaction across agents, models, tools, functions, and API boundaries without relying on verbose log text. Microsoft Foundry uses OpenTelemetry-compatible traces and Application Insights so related spans can be correlated into one execution path. Correlation identifiers allow operators to isolate one conversation and inspect where latency, failures, or unexpected behavior originated. The second requirement is different: it asks for recurring, comparable measurements of deployed response quality and safety. Scheduled evaluations against stable test datasets provide that longitudinal benchmark and are more appropriate than ad hoc tracing or infrastructure- only dashboards. Together, OpenTelemetry answers "what happened in this run?" while scheduled evaluation answers "is behavior getting better or worse over time?" The selected pair therefore covers both operational diagnosis and quality regression monitoring. For operational use, the measurement should be captured in a repeatable dataset, trace, or automated gate so that the same criterion can be compared across versions. That is more useful than a one-off manual observation and makes regressions visible before they become production incidents.
Official Microsoft reference: Microsoft Foundry observability concepts
NEW QUESTION # 72
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