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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architect multi-agent solutions | 15-20% | - Specify technology components for multi-agent solutions
|
| Topic 2: Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Implement observability and monitoring
|
| Topic 3: Develop multi-agent solutions in Azure | 30-35% | - Design and implement advanced prompt engineering strategies
|
| Topic 4: Secure, govern, and deploy multi-agent solutions | 20-25% | - Design and implement guardrails
|
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NEW QUESTION # 74
You have a Microsoft Foundry multi-agent customer support solution. The solution includes an orchestrator that starts a single conversation trace when a user request arrives and can invoke two agents named Agent1 and Agent2 concurrently. Each agent can invoke Model Context Protocol (MCP)-hosted tools and dependent REST APIs.
The solution uses OpenTelemetry SDKs to emit logs, metrics, and traces via OTLP to an OpenTelemetry Collector. The solution emits the conversation ID, trace ID, and span ID for agent invocations, tool invocations, and external API calls.
You have a monitoring dashboard that includes:
* Latency
* Throughput
* Reliability
* Token usage
* Content safety triggers
* Time to First Token (TTFT)
Alert rules are configured for latency anomalies and tool invocation failures.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
No / Yes / Yes
Aggregate dashboard metrics do not provide enough execution context to reproduce a specific unexpected model output, so the first statement is false. Reproduction normally requires detailed trace data such as model inputs/outputs, ordered agent spans, tool calls, and dependency results. The second statement is true because the solution emits conversation, trace, and span identifiers across agent, MCP tool, and REST API calls.
OpenTelemetry correlation can therefore isolate the Agent1 tool span that contributed to a latency spike in one conversation. The third statement is also true: latency-anomaly alerts and tool-invocation failure alerts cover important reliability signals from both agent execution and dependent service interactions. They are not the entire observability strategy, but they do address the reliability conditions described. The correct sequence is No, Yes, Yes. The evaluation should also preserve correlation identifiers and version information where possible so a failed score can be traced back to the exact agent, model, tool call, or retrieval step that produced it. This turns the metric into an actionable diagnostic rather than only a dashboard number.
Official Microsoft reference: Microsoft Foundry - agent tracing concepts
NEW QUESTION # 75
You have a Microsoft Foundry multi-agent solution.
Historical chat logs are limited and include customer Personally Identifiable Information (Pll).
The agents frequently produce invalid arguments when they call APIs by using structured function calls.
You need to create an initial fine-tuning dataset to improve the API-call behavior. The solution must minimize privacy exposure.
How should you configure the pipeline? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Initial example source: Synthetic generation; Generation task type: Tool use.
The historical chat logs are both sparse and privacy-sensitive, so using them directly as the initial fine-tuning corpus creates unnecessary PII exposure. Microsoft Foundry synthetic-data generation is intended to create diverse training examples when production data is limited and can avoid carrying customer identifiers into the dataset. The specific behavior that needs improvement is structured API/function calling, so the generator should use the Tool use task type. Tool-use generation can use an API/OpenAPI definition to create conversations that include valid tool selection and parameter construction, which directly trains the failure mode described. A general Q & A generator would not systematically teach function-call schemas. Therefore Synthetic generation plus Tool use is the configuration that both targets invalid arguments and minimizes privacy exposure. 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: Microsoft Foundry - synthetic fine-tuning data generation
NEW QUESTION # 76
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 # 77
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 # 78
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: A
NEW QUESTION # 79
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