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Zorluk: OrtaContainer-Based Workloads

A logistics company is designing a container-based processing solution for IoT telematics data. The solution requires:
- A public-facing API that receives sensor telemetry, which must scale dynamically based on concurrent HTTP requests.
- A batch processing workload that aggregates telemetry data every evening, which takes between 15 and 45 minutes to execute.
- All workloads must run in a secure environment with no public internet access for the backend processing components.
- The design must minimize administrative and operational effort.

Which two compute configurations should you recommend? (Select two.)

  1. An Azure Container Apps container app to host the public-facing APICevap
  2. An Azure Container Apps job to host the batch processing workloadCevap
  3. C
    An Azure Kubernetes Service (AKS) cluster to host both workloads
  4. D
    An Azure Functions consumption plan to host the batch processing workload

Cevap

To satisfy the requirements with the lowest operational overhead, you should recommend deploying the public-facing API as an Azure Container Apps container app and the batch processing workload as an Azure Container Apps job.
The correct recommendation is to use an Azure Container Apps container app for the public-facing API and an Azure Container Apps job for the batch processing workload. Both resources run within the same Azure Container Apps environment, allowing them to share a secure private virtual network. This satisfies the requirement to minimize administrative effort by utilizing serverless container hosting, while accommodating the 45-minute execution duration of the batch job.

Adım Adım Çözüm

1
Evaluate the administrative overhead requirements for the containerized workloads.
Azure Kubernetes Service (AKS) is eliminated because it introduces significant operational and cluster management overhead, which fails the requirement to minimize administrative effort.
Azure Container Apps provides a serverless platform that simplifies container deployments compared to full Kubernetes cluster management.
2
Analyze the execution lifetime constraint of the nightly batch processor.
An Azure Functions Consumption plan is ruled out because its execution timeout limit is 10 minutes, whereas the batch processor runs for 15 to 45 minutes.
Workloads must be matched to hosting options that support their execution durations without timing out.
3
Identify the appropriate Azure Container Apps deployment types for the two workloads.
The public-facing API is mapped to a standard container app (which scales based on HTTP traffic), and the nightly batch process is mapped to a container apps job (which is optimized for run-to-completion batch tasks).
This configuration utilizes the appropriate built-in features of Azure Container Apps for both transactional and batch processing workloads.

Anahtar Kavram

Selecting the appropriate Azure container service and workload type based on operational overhead, scale triggers, and execution lifetime.
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Container-Based Workloads

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