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Difficulty: Very hardContainer-Based Workloads

A healthtech company is designing a containerized genomic analysis system on Azure. The system has the following requirements:
- A front-end web portal and internal API microservices must scale dynamically based on HTTP traffic and queue-based events using KEDA.
- Background worker containers must process genomic sequences, taking up to 3 hours per execution. These workers must run to completion and automatically release resources when idle.
- The compute hosting solution must minimize administrative overhead and avoid managing container orchestrators.
- For compliance and data sovereignty, the application logs of the genomic background workers must be strictly segregated from the front-end portal logs, ensuring complete data isolation, separate lifecycle retention, and regional isolation.

Which three recommendations should you include in the solution?

  1. Deploy the front-end web portal and API microservices to Azure Container Apps.Answer
  2. Deploy the background worker containers as Azure Container Apps jobs.Answer
  3. Create separate Log Analytics workspaces to store the logs of the front-end portal and the background workers.Answer
  4. D
    Deploy the front-end web portal and background worker containers to an Azure Kubernetes Service (AKS) cluster.
  5. E
    Deploy the background worker containers to Azure Functions on a Consumption plan.
  6. F
    Deploy a single centralized Log Analytics workspace and use Azure RBAC to restrict access to logs.

Answer

To host the front-end portal and API microservices, deploy them to Azure Container Apps. To host the background worker containers, deploy them as Azure Container Apps jobs. For logging, create separate Log Analytics workspaces to satisfy strict compliance and data isolation requirements.
The correct recommendations involve using Azure Container Apps for the front-end and microservices to support serverless KEDA-based scaling with low administrative overhead, using Azure Container Apps jobs for the 3-hour run-to-completion background workers to avoid timeouts, and utilizing separate Log Analytics workspaces to ensure strict compliance, sovereignty, and data isolation boundaries for genomic logs.

Step-by-Step Solution

1
Analyze the compute requirements for the web portal and microservices.
Identify that KEDA scaling is required, and administrative overhead must be minimized.
Azure Container Apps provides serverless container hosting with native KEDA integration, satisfying these requirements without the overhead of AKS.
2
Analyze the compute requirements for the genomic background workers.
Identify that the tasks take up to 3 hours to process and must run to completion.
Standard Azure Container Apps are suited for continuous services, whereas Azure Container Apps jobs are designed for run-to-completion batch processing and support execution times up to 24 hours. Azure Functions on a Consumption plan is ruled out due to its 10-minute timeout limit.
3
Analyze the compliance and data isolation logging requirements.
Identify that strict segregation, regional sovereignty, and separate data lifecycle retention policies are required.
A single Log Analytics workspace cannot guarantee compliance boundaries at a sovereignty level; separate workspaces are required.
4
Synthesize the design recommendations.
Azure Container Apps for services, Azure Container Apps jobs for the background workers, and separate Log Analytics workspaces for logging.
This combination satisfies all requirements, respects the 3-hour processing constraint, enforces log segregation, and minimizes management overhead.

Key Concept

Selecting the appropriate Azure container hosting and monitoring architectures based on operational overhead, scaling behavior, execution duration, and compliance requirements.
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Topics

Container-Based Workloads

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