A financial technology (fintech) corporation is designing a container-based credit scoring engine. The engine will run as regional microservices deployed in both the Azure North Europe and US East regions to process local credit profiles.
The design must satisfy the following requirements:
* The compute platform must automatically scale replicas based on CPU utilization and must scale down to zero replicas during off-peak hours to minimize idle costs.
* The administrative effort required to manage host operating systems, Kubernetes control planes, and orchestrator upgrades must be minimized.
* Container logs must be collected and segregated to comply with strict regional data sovereignty regulations, ensuring that European credit profile logs are never stored in or co-mingled with US-based logs.
Which solution should you recommend?
- Deploy the regional microservices to internal Azure Container Apps environments in their respective regions. Configure separate Log Analytics workspaces in each region to store and isolate the container logs.Cevap
- BDeploy the regional microservices to Azure Kubernetes Service (AKS) clusters in each region. Configure a single centralized Log Analytics workspace in the primary region to consolidate all container logs.
- CDeploy the regional microservices to internal Azure Container Apps environments in their respective regions. Configure a single centralized Log Analytics workspace in the primary region with Table-level Role-Based Access Control (RBAC) to restrict access.
- DDeploy the regional microservices to Azure Container Instances (ACI) mapped to private virtual networks in each region. Configure separate Log Analytics workspaces in each region to store and isolate the container logs.