Microsoft AI-500 : Designing and Implementing Multi-Agent AI Solutions

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Microsoft AI-500 Exam Syllabus Topics:

Microsoft Designing and Implementing Multi-Agent AI Solutions Sample Questions:

Question #1
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?

A. Test a small set of jailbreak prompts by using Try in Playground Review the displayed risk messages and tune the highest-triggered categories.
B. Run labeled benign and adversarial test cases through each workflow path and evaluate the results from annotations and logs.
C. Review the Guardrails tab in the Compliance workspace, export deployment configurations, and mark each deployment as Compliant or Noncompliant
D. Update the guardrail to annotate-only mode, collect live user interactions, and summarize blocked and annotated events after rollout

Question #2
You have a multi-agent customer support solution in a Microsoft Foundry project.
You have a dataset that contains query, context, and response without document relevance labels.
You need to implement built-in evaluators that provide 1 to-5 scores with pass/fail labels for the following metrics:
* The quality of the retrieved context
* How directly a response answers a query
Which evaluator should you use for each metric? To answer, drag the appropriate evaluators to the correct metrics. Each evaluator 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.

Question #3
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?

A. Persist the workflow thread in Azure Cosmos DB and retrieve transcript excerpts during each handoff.
B. Use a larger-context model and retain an accumulated workflow summary for handoffs
C. Give each subagent a GitHub issue lookup tool and continue injecting a shared summary.
D. Build each handoff context from the current GitHub issue state and discard invalidated memory.

Question #4
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?

A. Azure Kubernetes Service (AKS)
B. Microsoft Foundry Agent Service
C. Azure Container Instances
D. GPU-enabled Azure virtual machines

Question #5
You have a Microsoft Foundry multi-agent solution. The solution includes a parent agent that can call an Azure logic app and delegate to two subagents.
You need to implement a review process for flagged interactions. The solution must meet the following requirements;
* Identify requests that call third-party services.
* Moderate the prompts, steps, and tool calls.
* Include a governance review.
What should you do?

A. Use Foundry Content Safety to prioritize sensitive messages for moderators, require moderator approval for final responses, and allow selected calls to proceed automatically.
B. Submit the requests by using central sensitive-use intake, enable guardrails and traces, and require reviewers to approve, edit, or reject messages.
C. Route subagent findings to compliance reviewers, correlate the parent and subagent. and require approval for only the final parent-agent message.
D. Run agent evaluators during CI/CD, require a reviewer to approve the release, and collect production transcripts for sensitive-use intake after the pilot.

Solutions: