Focus on this objective today: Optimize enterprise-scale semantic models: Configure Direct Lake, including default fallback and refresh behavior. Explain it in your own words, apply it to one realistic scenario, and verify the details against the official source. Source context: Read in English Design enterprise-scale agent communication with A2A in Azure Module 8 Units Feedback Advanced AI Engineer Solution Architect Azure Microsoft Foundry Foundry Agent Service Azure Cosmos DB Design enterprise-scale agent communication architectures using the A2A protocol in Azure. Implement agent discovery registries with capability-based routing, design shared state management using Azure Cosmos DB and Azure Managed Redis, configure context isolation for multitenant deployments, and build conflict detection and resolution mechanisms for collaborative agent networks. Learning objectives By the end of this module, you're able to: Design A2A agent ecosystems with discovery registries and dynamic capability-based routing Implement shared state management strategies across distributed agent teams using Azure services Configure context isolation and sharing patterns for multitenant agent deployments Build conflict detection and resolution mechanisms for collaborative agent networks Add Prerequisites Before starting this module, you should have: Experience with the A2A protocol for agent discovery and connection from the Discover Azure AI Agents with A2A module or equivalent Familiarity with Azure Cosmos DB and Azure Managed Redis Experience building multi-agent solutions with Azure AI Foundry Agent Service Proficiency in Python Get started with Azure Choose the Azure account that's right for you.
Objective
Optimize enterprise-scale semantic models: Configure Direct Lake, including default fallback and refresh behavior