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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architect multi-agent solutions | 15-20% | - Specify technology components for multi-agent solutions
|
| Topic 2: Secure, govern, and deploy multi-agent solutions | 20-25% | - Deploy multi-agent solutions to Azure
|
| Topic 3: Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Design and implement evaluation and validation strategies
|
| Topic 4: Develop multi-agent solutions in Azure | 30-35% | - Build and integrate tool ecosystems
|
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質問 # 39
You have a Microsoft Foundry multi-agent solution. The solution includes an orchestrator agent that sends a mix of simple requests, reasoning-heavy tasks, and tool-calling workflows to a single premium model deployment.
Response quality is acceptable, but costs are too high, and latency varies significantly across requests.
You need to recommend a solution to reduce token utilization based on the complexity of the user input.
What should you include in the recommendation?
正解：D
解説：
The workload mixes simple prompts, reasoning-heavy tasks, and tool-calling workflows, so a single premium model is over-provisioned for many requests. Microsoft Foundry ' s model router evaluates request complexity and routes each request to an appropriate model from a configured pool. Simple work can use lower-cost, lower-latency models, while complex reasoning can still be assigned to a more capable model.
Structured outputs constrain response format but do not reduce model capability according to request complexity. Prompt Optimizer can improve prompt quality but does not perform per-request model selection, and choosing another flagship model would preserve the same all-requests-use-premium pattern. Therefore A directly addresses the cost and latency variability by matching model capacity to workload complexity. 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 - Model router
質問 # 40
You have a multi-agent solution hosted in an Azure App Service plan.
You use Azure App Configuration to store runtime settings.
A release adds a new planner agent and changes the prompt routing
You need to recommend a release plan that meets the following requirements:
* Slowly rolls out the release to users
* Uses end-user health alerts as rollout gates
* Increases exposure without redeploying the application
* Limits production exposure until the health metrics remain green
* Implements the solution without deploying a second environment
What should you recommend? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Methodology: Canary deployment; Exposure control: Azure App Configuration feature flags.
A canary release deliberately exposes a new behavior to a limited subset of users and expands that exposure only when operational health remains acceptable. That directly matches the requirement to use end-user health alerts as rollout gates. Azure App Configuration feature flags provide the runtime control needed to change the exposed percentage or audience without redeploying the application. Microsoft documents feature management as a way to decouple deployment from release, which is essential here because the application must stay in one environment while exposure changes. Blue/green deployment normally maintains a second production-like environment or slot, conflicting with the stated constraint. DTAP describes environment progression but not the requested gradual production exposure. Therefore canary plus App Configuration feature flags satisfies gradual rollout, health gating, dynamic exposure, and the no-second-environment requirement. Least privilege remains the governing principle: grant only the identity, data, tool, or deployment access required for the specific operation. The selected answer preserves that boundary while still allowing the workflow to satisfy its functional requirement.
Official Microsoft reference: Azure App Configuration - feature management
質問 # 41
You have the following tool integration configuration for a Microsoft Foundry claims-processing assistant.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Yes / Yes / No
The first statement is true because the tool-integration contract returns only the explicitly defined `final_text` from FraudReview to ClaimsPlanner; the planner does not automatically receive the specialist ' s entire internal execution history. The second statement is also true because an external REST service such as PremiumRates can be exposed as an OpenAPI-defined tool. The OpenAPI schema describes operations and parameters, while the surrounding agent system retains conversational state; the REST tool itself does not need a developer-managed chat transcript. The third statement is false under the shown configuration because the partner publishes tool descriptors as published while the planner defines which schemas it accepts or rejects. Compatibility handling therefore occurs at the consumer/tool boundary rather than requiring PartnerIntegrations to rewrite every unsupported schema before invocation. The correct sequence is Yes, Yes, 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: Microsoft Foundry agents - OpenAPI tools
質問 # 42
You need to implement an advanced prompt engineering strategy to resolve the Patient Intake agent issues.
The solution must prevent hardcoding new logic into the agent ' s core prompt.
What should you do?
正解：A
解説：
The intake problem involves verbose, variable patient narratives and a requirement to avoid hardcoding new logic into the core prompt. Injecting dynamic context with curated few-shot examples gives the model representative demonstrations of how to identify clinically relevant symptoms while ignoring irrelevant narrative details. Microsoft AI-500 objectives explicitly include examples and dynamic context injection as advanced prompt-engineering techniques. Removing defensive guidance would weaken safety. Reducing the context window would not teach the model what information matters and could simply discard useful details.
Frequent full-model fine-tuning is operationally heavier and unnecessary for a behavior that can be demonstrated through curated examples. Dynamic few-shot context also allows the examples to be versioned and updated without rewriting the agent ' s base persona or application logic. Therefore C best addresses the extraction problem while preserving prompt lifecycle flexibility. 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: AI-500 Study Guide - advanced prompt engineering
質問 # 43
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?
正解：C
解説：
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
質問 # 44
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