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Difficulty: HardContainer-Based Workloads

A medical diagnostics company is designing a container-based data processing solution on Azure. The solution will process patient telemetry records from multiple regions. The solution must satisfy the following requirements:
- The data processing tasks are short-lived, containerized workloads that run to completion and execute only when new telemetry records arrive in an Azure Service Bus queue.
- The compute infrastructure must automatically scale to zero when the queue is empty, and scale out dynamically based on the queue depth using Kubernetes Event-driven Autoscaling (KEDA).
- Administrative overhead associated with managing virtual machines, orchestrator nodes, and host patching must be minimized.
- Compliance regulations dictate that patient telemetry logs must be stored and isolated within their respective geographical regions (Europe and North America).

Which two configurations should you include in the design?

  1. Azure Container Apps Jobs to host and execute the event-driven data processing tasks.Answer
  2. Two separate Log Analytics workspaces, one deployed in Europe and one in North America, to collect and isolate regional telemetry logs.Answer
  3. C
    An Azure Kubernetes Service (AKS) cluster configured with the KEDA add-on to run the processing tasks.
  4. D
    A single centralized Log Analytics workspace configured with table-level Access Control Lists (ACLs) to store all telemetry logs.

Answer

The design should include Azure Container Apps Jobs to run the event-driven data processing tasks, and two separate Log Analytics workspaces (one in Europe and one in North America) to meet regional data sovereignty and isolation requirements.
The solution requires a serverless compute platform that runs containerized, short-lived, event-driven tasks to completion while minimizing operational overhead. Azure Container Apps Jobs natively support KEDA scaling, scale to zero, and offload host management. Additionally, regional data sovereignty rules require logs to be physically isolated and stored within their respective boundaries, necessitating two separate Log Analytics workspaces (one in Europe and one in North America).

Step-by-Step Solution

1
Evaluate the compute options based on workload pattern and operational overhead constraints.
Identify that the workloads are short-lived, containerized tasks that run to completion and require KEDA. Azure Container Apps Jobs provide serverless execution with native KEDA triggers, eliminating node-level management compared to Azure Kubernetes Service (AKS).
This step eliminates options that introduce unnecessary management overhead like AKS.
2
Analyze the logging and regulatory requirements for data sovereignty.
Determine that telemetry logs must be physically stored and isolated in separate jurisdictions (Europe and North America). This requires deploying regional Log Analytics workspaces rather than a centralized one.
This step ensures compliance with data residency and sovereignty requirements.

Key Concept

Selecting serverless container services (Azure Container Apps Jobs) over full Kubernetes clusters (AKS) to reduce operational overhead for event-driven, short-lived tasks, and designing regional Log Analytics workspaces to satisfy regional data residency and compliance rules.
Estimated Time:3m 0s
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Topics

Container-Based Workloads

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