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Difficulty: HardSelecting and Designing Compute and Application Platforms

A biopharmaceutical research organization is modernizing its genomic sequence processing platform on Google Cloud. The platform consists of two distinct workloads:

1. Stateless API Gateway: A lightweight HTTP webhook service that receives small metadata payloads from laboratory sequencers and enqueues jobs. Traffic is highly unpredictable, experiencing intense bursts followed by extended idle periods (down to zero requests) during non-business hours. The organization demands zero management overhead for server administration.

2. Bio-Analytics Pipeline: A high-performance computation engine executing long-duration batch workloads (up to 18 hours per execution). This engine relies on legacy C++ binaries compiled with specialized Linux kernel patches and requires mounting a POSIX shared memory file system (`/dev/shm`) allocated at 128 GB.

Which TWO compute platform architectural choices should you implement to satisfy the technical requirements while minimizing operational cost and management overhead? (Select TWO.)

  1. Deploy the Stateless API Gateway on Cloud Run, configuring minimum instances to zero and allowing HTTP traffic to scale dynamically based on request volume.Answer
  2. B
    Deploy the Stateless API Gateway on a dedicated Google Kubernetes Engine (GKE) Autopilot cluster configured with a fixed minimum node count to prevent cold starts during idle periods.
  3. C
    Deploy the Bio-Analytics Pipeline as Cloud Run Jobs, setting container memory limits to 128 GB and configuring job execution timeout to 24 hours.
  4. Deploy the Bio-Analytics Pipeline on Compute Engine Managed Instance Groups (MIGs) using custom VM images pre-loaded with the modified Linux kernel.Answer
  5. E
    Purchase 3-year Committed Use Discounts (CUDs) covering the peak auto-scaled instance capacity for the Stateless API Gateway.

Answer

The optimal architecture requires deploying the Stateless API Gateway on Cloud Run (scaling to zero to eliminate idle operational costs and administration overhead) and hosting the Bio-Analytics Pipeline on Compute Engine Managed Instance Groups (MIGs) using custom VM images to support specialized Linux kernel modifications and large POSIX shared memory allocations.
The correct architectural choices combine Cloud Run for the stateless web service and Compute Engine for the kernel-dependent analytics engine. Cloud Run provides a serverless execution environment that automatically scales down to zero when idle, eliminating management overhead and baseline costs for the webhook API. Compute Engine VMs allow custom OS images with specialized Linux kernel patches and configurable system memory layout (`/dev/shm`), satisfying the strict low-level system requirements of the C++ bio-analytics pipeline.

Step-by-Step Solution

1
Evaluate the Stateless API Gateway requirements
The service is stateless, handles HTTP webhooks, has unpredictable bursty traffic with periods of zero volume, and requires zero administration overhead.
Cloud Run is the optimal compute platform for stateless HTTP services with scale-to-zero capabilities and fully managed serverless infrastructure.
2
Evaluate the Bio-Analytics Pipeline requirements
The workload requires custom Linux kernel patches, long execution times (18 hours), and a 128 GB POSIX shared memory mount (`/dev/shm`).
Serverless container platforms (such as Cloud Run or Cloud Run Jobs) run on standardized managed kernels that do not permit low-level host kernel customization. Compute Engine VMs provide full root access and OS kernel customization capabilities.
3
Assess cost optimization and distractor trade-offs
GKE Autopilot adds baseline management overhead for simple microservices; CUDs covering peak capacity for bursty workloads lead to over-provisioning spend; container platforms cannot host custom kernel patches.
Matching workload operational characteristics to platform capabilities ensures architectural compliance with Google Cloud best practices.

Key Concept

Compute Platform Selection Strategy (Cloud Run vs. Compute Engine)
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Topics

Selecting and Designing Compute and Application Platforms