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| Section | Weight | Objectives |
|---|---|---|
| Develop multi-agent solutions in Azure | 30-35% | - Implement multi-agent orchestration
|
| 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 security for multi-agent solutions
|
| Evaluate, optimize, and monitor multi-agent solutions | 20-25% | - Optimize prompt and model performance
|
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NEW QUESTION # 34
You have a Microsoft Foundry multi-agent solution.
A developer publishes a new version of a specialist agent. Once the agent goes live in production, the solution starts mishandling requests.
You need to restore the previous behavior as quickly as possible
What is the fastest way to roll back the agent?
Answer: D
Explanation:
The fastest safe rollback is to route the stable endpoint back to the previous known-good immutable agent version. Current Foundry lifecycle guidance supports versioned agents and endpoint/version selection so production traffic can be redirected without rebuilding the agent from scratch. Deleting the newly published version is a destructive cleanup action and is not the preferred rollback mechanism because it removes an artifact that may be needed for diagnosis. Creating a new agent changes the lifecycle identity and takes longer, while a complete redeployment is unnecessary if the earlier version already exists. Option C is therefore correct when interpreted as changing the endpoint ' s active-version or version-selector configuration to the previous version while keeping the endpoint URL stable. 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 agents - development lifecycle and versioning
NEW QUESTION # 35
You are validating the outcome of the consultant proposal for the Patient Intake agent.
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 / No / No
All three consultant-proposal statements should be rejected. Applying the highest sensitivity to every guardrail control is not automatically safer because it can increase false positives and unnecessarily block legitimate clinical interactions; controls should be tuned to the actual risk and tested. Telling the model in a system prompt to ignore hate speech is not equivalent to enabling the platform content-safety controls required for public-facing agents. Finally, repeatedly prompting patients to verify symptoms does not address long-context growth and can worsen token usage and latency. Microsoft guidance separates deterministic safety controls, evaluation, and context-management mechanisms from ordinary prompt instructions. The proposal also hardcodes medical behavior in prompts, contrary to the requirement to avoid embedding new clinical logic in the core prompt. A robust design would use appropriate content-safety/guardrail controls, validated intervention points, and a deliberate memory/compaction strategy rather than relying on increasingly restrictive or repetitive prompt text. The same configuration should be paired with auditable identity, trace, and evaluation data so reviewers can prove which principal acted, which policy was applied, and why a request was allowed or blocked. That is particularly important for production multi-agent systems with external tools.
Official Microsoft reference: AI-500 Study Guide - guardrails, context management, and evaluation
NEW QUESTION # 36
You have a Microsoft Foundry project that contains an incident triage agent.
You have a Model Context Protocol (MCP) server registered in the organizational tool catalog. The MCP server exposes two tools named docs_search and deployment_delete.
You need to ensure that the agent can only invoke docs_search.
What should you configure?
Answer: D
Explanation:
The restriction belongs in the agent ' s MCP tool configuration because Microsoft Foundry supports an
`allowed_tools` allowlist that controls which tools discovered from an MCP server are exposed to the agent.
Configuring the allowlist to include only `docs_search` makes `deployment_delete` unavailable for model selection. This is stronger than adding a sentence to the agent instructions because instructions influence behavior but do not remove a dangerous tool from the callable surface. Project details describe resources rather than per-agent tool exposure, and a transient run setting is not the appropriate persistent configuration boundary for the registered MCP integration. Therefore C, the agent tool configuration, is the correct answer.
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. 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 agents - Model Context Protocol tools
NEW QUESTION # 37
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 # 38
You have a multi-agent solution in a Microsoft Foundry project. The project connects to an Azure Storage account named stgaudit.
You plan to enable a storage-backed tool for the agent The tool will read and write blobs to stgaudit.
You need to create a role assignment for the agent. The solution must follow the principle of least privilege.
Which role should you use?
Answer: B
NEW QUESTION # 39
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