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Difficulty: EasySelecting and Designing Compute and Application Platforms

A healthcare technology company is building a cloud application on Google Cloud that comprises two distinct service workloads:

1. A stateless HTTP REST API microservice that receives unpredictable web traffic, requiring rapid scaling and the ability to scale down to zero when idle.
2. A specialized data stream processing agent that requires installing custom Linux host kernel modules for hardware acceleration.

Which of the following compute platform decisions should the Cloud Architect recommend to meet these requirements with optimal operational efficiency and minimal cost? (Select TWO choices.)

  1. Deploy the stateless HTTP REST API microservice on Cloud Run to achieve automatic scaling to zero and eliminate infrastructure management overhead.Answer
  2. Deploy the specialized data stream processing agent on Compute Engine Virtual Machines using a custom OS image to support custom Linux kernel module installation.Answer
  3. C
    Deploy the stateless HTTP REST API microservice on a GKE Autopilot cluster to run containerized microservices with zero underlying node management.
  4. D
    Deploy the specialized data stream processing agent on Cloud Run by passing custom host kernel parameters inside the container deployment manifest.
  5. E
    Purchase 3-year Committed Use Discounts (CUDs) for the stateless HTTP microservice baseline compute resources prior to deployment.

Answer

The optimal architectural design requires deploying the stateless HTTP REST service on Cloud Run (for automatic scale-to-zero capability and minimal operational overhead) and deploying the kernel-dependent processing agent on Compute Engine Virtual Machines (to allow host OS kernel module installation).
Cloud Run is the recommended platform for stateless HTTP services with unpredictable traffic because it scales down to zero instances when idle, minimizing compute cost while removing cluster provisioning overhead. Compute Engine virtual machines are necessary for workloads requiring host-level modifications, such as loading custom Linux kernel modules, because managed container platforms enforce sandbox isolation that prevents direct host kernel modification.

Step-by-Step Solution

1
Analyze the requirements for the stateless HTTP microservice
Select Cloud Run because it natively supports containerized stateless HTTP APIs, scales automatically from zero to meet demand spikes, and incurs zero cost when idle.
Fully managed serverless container execution eliminates cluster management overhead for simple stateless web endpoints.
2
Analyze the requirements for the custom kernel driver workload
Select Compute Engine VMs with custom images because low-level kernel drivers require direct host operating system access.
Managed serverless container platforms restrict host kernel access and do not permit loading custom kernel modules.

Key Concept

Selecting GCP Compute Platforms based on workload statefulness, operational overhead, autoscaling behavior, and OS-level customization needs
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Topics

Selecting and Designing Compute and Application Platforms