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| Section | Weight | Objectives |
|---|---|---|
| Develop multi-agent solutions in Azure | 30-35% | - Design and implement advanced prompt engineering strategies
|
| Architect multi-agent solutions | 15-20% | - Design logical architecture for multi-agent solutions
|
| Secure, govern, and deploy multi-agent solutions | 20-25% | - Design and implement guardrails
|
| Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Evaluate solution quality
|
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NEW QUESTION # 31
You have a LangGraph multi-agent workflow that uses Azure Cosmos DB as a checkpointer.
You plan to change the pruning rules for the running message list before the next release You need to implement evaluations tor memory. The solution must meet the following requirements:
* Verify that claim-related state values remain correct at transition points.
* Verify that conversation context is preserved after an interruption.
* Identify the source of any loss of continuity during a run
Which three actions should you perform Each correct answer presents part of the solution NOTE: Each correct selection is worth one point.
Answer: B,C,F
Explanation:
Memory evaluation must verify persistence across interruption, correctness of checkpointed state, and enough telemetry to locate where continuity was lost. Running a scripted multi-turn case with one stable `thread_id` before and after a restart directly tests whether the Cosmos-backed LangGraph checkpointer restores the same conversation/workflow. Comparing persisted checkpoint documents with expected state values at each handoff verifies that claim-related state has not been corrupted by the new pruning rules. Correlating the evaluation run with trace spans then provides the diagnostic path to identify which agent transition or state- management operation caused a failure. Changing the thread ID each turn would intentionally create separate state scopes, and removing durable artifacts would defeat the persistence test. Tool-call arguments alone do not prove workflow-memory continuity. Therefore D, E, and F are the complementary actions required. 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: Azure Cosmos DB integrations for LangGraph and agent state
NEW QUESTION # 32
You need to recommend an end-to-end release lifecycle for claim Approval. The solution must meet the technical requirements.
What should you recommend for each requirement? To answer, drag the appropriate recommendations to the correct requirements. Each recommendation 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:
Provision Test, Acceptance, and Production by using a Bicep file applied through the CI/CD pipeline in Azure Pipelines; block promotion when quality falls below the threshold by using Foundry Agent Evaluator as a pipeline release gate.
The release lifecycle must satisfy two independent requirements: repeatable infrastructure deployment across DTAP environments and an automated quality gate before promotion. Bicep is Azure ' s infrastructure-as- code language, and Microsoft documents deploying Bicep through Azure Pipelines so the same declarative resources can be promoted consistently across subscriptions. For the promotion decision, an automated Foundry evaluation is the appropriate control because response quality is an AI-system property that should be measured against a defined threshold before the next stage is released. A manual portal deployment would weaken repeatability, while a deployment-only gate would not measure model or agent behavior. The important design point is that infrastructure provisioning and AI quality validation are separate controls in the same pipeline: Bicep establishes environment parity, and the evaluation gate prevents a technically deployable but behaviorally degraded agent version from advancing. 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: Deploy Bicep files with Azure Pipelines; AI-500 Study Guide
NEW QUESTION # 33
You have a Microsoft Foundry multi-agent customer support solution that routes requests from an intake agent to a retrieval agent, and then to a resolution agent. A custom guardrail is assigned to the agents.
You discover that some legitimate support requests are blocked, and some injected instructions in retrieved documents are allowed.
You need to validate the updated guardrail. The solution must meet the following requirements:
* Identify false positives and false negatives.
* Verify policy coverage across the agent solution
* Prevent changing the production agent behavior during testing
* Measure intervention accuracy by using a control and intervention point.
What should you do?
Answer: D
Explanation:
The updated guardrail must be evaluated with known benign and adversarial cases so false positives and false negatives can be measured explicitly. Running labeled cases through every relevant workflow path also tests whether the guardrail is applied consistently at the intended intervention points. Microsoft AI-500 guidance includes guardrail testing with synthetic or curated data and emphasizes evaluation before production rollout.
Playground-only spot checks are too narrow to establish coverage. Switching production to annotate-only would change live behavior and use customers as the test population, violating the requirement to avoid production changes. A compliance configuration review verifies that a policy exists but does not measure whether it correctly detects or misses real inputs. Option B is therefore the only approach that produces repeatable evidence of intervention accuracy and policy coverage without changing production behavior. 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: AI-500 Study Guide - guardrail testing and evaluation
NEW QUESTION # 34
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?
Answer: D
Explanation:
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
NEW QUESTION # 35
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: B
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 # 36
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