You are designing an enterprise automation solution that has a web portal for users and the following types of workflows:
* Routine workflows that use prompt-defined agents, approved knowledge stores, and HTTPS tools
* Modernization workflows that use custom Microsoft Agent Framework code packaged as container images with predefined handoffs and task states You need to recommend Azure services that meets the following requirements:
* The routine workflows must run agents in a fully managed environment.
* The user portal must be deployed as a managed platform as a service (PaaS) web app
* The modernization workflows must run agents in serverless containers that support automatic scaling What should you recommend for each requirement? To answer, drag the appropriate services to the correct components. Each service 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.

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

You have a Microsoft Foundry resource that hosts Azure OpenAI model deployments for three projects. Each project is for a different business unit. The projects share the same Foundry resource.
You need to implement a Microsoft Cost Management view that separates the shared model spend by the project The solution must meet the following requirements:
* Use cost data that can be reconciled by using Azure Cost Management.
* Minimize manual tagging.
What should you use?

• A. cost analysis scoped to the resource group with grouping by Meter and exports for each project
• B. cost analysis scoped to the Foundry resource with a tag filter on project
• C. cost analysis scoped to the Foundry resource with grouping by Resource and saved views for each project
• D. cost analysis scoped to the subscription with a Service tier: Azure OpenAI filter and budgets for each business unit

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