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| Section | Weight | Objectives |
|---|---|---|
| Architect multi-agent solutions | 15-20% | - Specify technology components for multi-agent solutions
|
| Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Design and implement evaluation and validation strategies
|
| Secure, govern, and deploy multi-agent solutions | 20-25% | - Design and implement guardrails
|
| Develop multi-agent solutions in Azure | 30-35% | - Implement multi-agent orchestration
|
>> AI-500 Quizfragen Und Antworten <<
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15. Frage
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?
Antwort: A
Begründung:
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
16. Frage
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?
Antwort: D
Begründung:
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
17. Frage
You have a Microsoft Agent Framework multi-agent workflow. The workflow includes a context provider that injects a condensed memory summary into each agent invocation. Requirements and acceptance criteria are stored in GitHub issues.
During a long-running workflow, the acceptance criteria changes. Later agents sometimes apply the earlier criteria. Token telemetry shows condensed summaries grow across handoffs. causing older content to be truncated.
You need to ensure that the later agents use the current requirements. The solution must limit context window growth.
What should you do?
Antwort: A
Begründung:
GitHub issues are the authoritative source for current requirements, and those requirements can change while a long-running workflow is still executing. Each handoff should therefore reconstruct context from the current issue state and discard memory that has been invalidated by newer requirements. Keeping an accumulated summary allows obsolete criteria to remain present and also causes the prompt to grow until older content is truncated. Persisting the transcript in Cosmos DB preserves history but does not make that history current.
Giving subagents a GitHub lookup tool while continuing to inject the stale summary can expose the model to conflicting versions of the same requirement. Microsoft Agent Framework context providers are intended to inject current external context at invocation time, making C the approach that simultaneously preserves freshness and bounds context growth. 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: Microsoft Agent Framework - Context providers
18. Frage
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.
Antwort:
Begründung:
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
19. Frage
You have a Microsoft Foundry agent that completes benefits enrollment during a single user conversation The agent collects the required enrollment fields during 15 turns. Users can correct earlier values before final submission. The current implementation sends the complete transcript with every model request.
You need to change the context accumulation strategy for the active enrollment. The solution must meet the following requirements:
* Preserve the latest value for each required enrollment field until submission.
* Bound the maximum number of tokens sent with each model request
* Preserve user corrections until submission.
* Prevent durable cross-session memory.
What should you do?
Antwort: C
Begründung:
The application must preserve the latest authoritative value of each enrollment field even when the user corrects an earlier value, while also placing a firm bound on prompt size. A session-scoped enrollment-state snapshot provides deterministic structured state: each correction overwrites the previous field value. A recent- turn sliding window then preserves enough conversational context for natural interaction without resending the full 15-turn transcript. Response chaining with a fixed number of prior turns can lose an important field once it falls outside the retained history. Summarization can omit or distort corrected values, and automatic truncation likewise offers no guarantee that the latest value of every required field survives. Because the state is scoped only to the active enrollment and is not written to long-term memory, the design also avoids durable cross-session memory. Therefore A 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: Microsoft Agent Framework - workflow state and context management
20. Frage
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