Question

Difficulty: MediumServerless and Web Application Hosting

A legal services company is implementing a serverless worker that compiles large case files into a single PDF document. The compilation process is CPU-intensive and typically runs for 11 to 14 minutes per request. The company wants a fully managed serverless compute option that scales to zero to minimize costs during periods of inactivity, and they want to avoid the administrative overhead of managing underlying container clusters or virtual machines. Which Azure service should you select to meet these requirements?

  1. A
    Azure Functions on a Consumption plan
  2. Azure Container Apps JobsAnswer
  3. C
    Azure Kubernetes Service (AKS) cluster
  4. D
    Azure Virtual Machines using Spot Instances

Answer

Azure Container Apps Jobs
Azure Container Apps Jobs are ideal for run-to-completion tasks that require containerized environments without the complexity of AKS. They support execution times beyond 10 minutes, trigger dynamically on events or schedules, scale to zero when idle to save costs, and are fully managed by Azure.

Step-by-Step Solution

1
Analyze the execution duration requirement.
The compilation runs for 11 to 14 minutes, which is longer than the 10-minute maximum limit of the standard serverless execution environment of Azure Functions on a Consumption plan.
Identifying runtime requirements helps prevent execution timeouts.
2
Evaluate administrative and management overhead limits.
The requirement specifies avoiding cluster or virtual machine management, ruling out self-managed solutions.
Choosing a fully managed service satisfies the operational overhead constraints.
3
Match the workload type to the optimal serverless execution model.
Azure Container Apps Jobs support long-running, event-triggered tasks that scale to zero, meeting all criteria.
Selecting the service designed for run-to-completion containerized tasks ensures reliability and cost-efficiency.

Key Concept

Selecting serverless hosting options based on execution limits and management overhead constraints.
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