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Difficulty: HardContainer-Based Workloads

A transit authority is designing a containerized telemetry processing system to monitor municipal bus fleets across two geographical sectors. The system consists of multiple microservices that ingest GPS coordinates and engine diagnostics from an Azure IoT Hub.

The architecture must meet the following requirements:
- Support event-driven scaling using KEDA based on message queue depth.
- Minimize administrative effort and operational overhead for managing the underlying compute infrastructure.
- Satisfy strict regional compliance regulations by ensuring that telemetry logs from each sector are stored exclusively within their respective Azure regions and are not co-mingled.

Which infrastructure design should you recommend?

  1. Deploy Azure Container Apps in each region, and configure a separate Log Analytics workspace in each region.Answer
  2. B
    Deploy Azure Kubernetes Service (AKS) clusters in each region, and configure a separate Log Analytics workspace in each region.
  3. C
    Deploy Azure Container Apps in each region, and configure a single centralized Log Analytics workspace to aggregate logs from both regions.
  4. D
    Deploy Azure Kubernetes Service (AKS) clusters in each region, and configure a single centralized Log Analytics workspace to aggregate logs from both regions.

Answer

Deploy Azure Container Apps in each region, and configure a separate Log Analytics workspace in each region.
The correct design uses Azure Container Apps because it natively supports KEDA-based scaling and minimizes operational and administrative overhead compared to managing full Kubernetes clusters. Additionally, deploying separate Log Analytics workspaces in each region satisfies the compliance requirement by ensuring data residency and preventing log co-mingling across different geographical jurisdictions.

Step-by-Step Solution

1
Evaluate the scaling and operational requirements for the compute platform.
Identify that Azure Container Apps supports event-driven scaling using KEDA natively and operates as a serverless platform, minimizing administrative overhead compared to Azure Kubernetes Service.
This addresses the requirement to support KEDA scaling while minimizing compute management effort.
2
Evaluate the compliance and logging requirements.
Determine that separate Log Analytics workspaces must be deployed in each region to ensure regional log isolation and compliance.
A single centralized workspace would co-mingle telemetry data from different sectors, violating the regional compliance constraints.
3
Combine the compute and logging evaluations to select the optimal design.
Select the combination of regional Azure Container Apps and separate regional Log Analytics workspaces.
This combined design satisfies both the low-administrative compute overhead constraint and the strict log isolation compliance mandate.

Key Concept

Designing serverless container workloads with regional data residency and compliance auditing.
Estimated Time:2m 30s
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Topics

Container-Based Workloads

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