[Jan Egil Ring (janegilring)]

This pull request introduces robust region fallback and feature registration logic for Adaptive Apps lab automation in Azure, improving reliability and clarity for both organizers and participants. The main changes add automatic handling of region failures during initial provisioning, enforce and verify required Azure feature registration, and update documentation and tests to reflect these improvements.

Region fallback and provisioning improvements:

• Initial infrastructure provisioning now supports an ordered list of preferred regions (e.g., swedencentral,spaincentral), automatically retrying in validated alternatives if the primary region fails. This is handled by Invoke-MhhDeploymentWithRegionFallback, which can delete and recreate resource groups as needed during initial setup, but not for protected labs. (README.md, lab-defaults.json, deploy-lab.ps1) [1] [2] [3] [4]
• Participant resource deployment enforces use of validated regions and prevents relocation of established labs, preserving resources and deployment records for diagnosis. (README.md, deploy-lab.ps1) [1] [2]

Azure feature registration and validation:

• The shared deployment script (shared-deploy-lab.ps1) now ensures the Microsoft.Network/AllowBringYourOwnPublicIpAddress feature is registered before re-registering the Network provider, with explicit waiting and error handling. This is required for public IP allocation in these scenarios. (shared-deploy-lab.ps1, README.md) [1] [2] [3]
• Improved error messages and handling for feature registration failures, timeouts, and quota issues, with clear guidance for organizers. (shared-deploy-lab.ps1, README.md) [1] [2]

Test and documentation updates:

• Unit tests have been expanded to cover region fallback logic, feature registration sequencing, and error handling for registration failures and timeouts. (console.Tests.ps1) [1] [2] [3] [4] [5]
• Documentation has been updated throughout to clarify the new region and feature registration logic, fallback behavior, and guidance for troubleshooting. (README.md) [1] [2] [3] [4]

These changes make the lab automation more resilient to regional capacity and registration issues, ensure compliance with Azure requirements, and provide clearer feedback to users.