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Difficulty: MediumSelecting and Designing Compute and Application Platforms

A healthcare software provider is designing a cloud-native SaaS platform on Google Cloud to host two distinct application components:

1. A stateless REST API backend that receives sporadic HTTP telemetry data from connected medical devices, experiences long idle periods, and must scale to zero instances to eliminate idle infrastructure costs.
2. An on-demand background data cleaning workflow that runs non-HTTP containerized scripts for up to 4 hours per execution cycle when triggered by batch ingestion events.

The enterprise architecture team mandates a solution that minimizes operational management overhead by avoiding virtual machine management, cluster provisioning, or control plane administration.

Which TWO Google Cloud compute platform configurations should the team select to fulfill these requirements? (Select TWO.)

  1. Deploy the stateless REST API backend to Cloud Run services configured with minimum instances set to zero.Answer
  2. Deploy the background data cleaning scripts to Cloud Run jobs configured to execute tasks on demand.Answer
  3. C
    Deploy the stateless REST API backend to a Google Kubernetes Engine (GKE) Autopilot cluster using a Horizontal Pod Autoscaler (HPA).
  4. D
    Deploy the background data cleaning scripts to App Engine Standard Environment using task queues with automatic scaling.
  5. E
    Deploy the stateless REST API backend to Compute Engine Managed Instance Groups (MIGs) behind an External HTTP(S) Load Balancer.

Answer

The optimal architecture combines Cloud Run services for the stateless REST API backend and Cloud Run jobs for the long-running background data cleaning workflow.
Cloud Run services provide fully managed serverless deployment for containerized stateless web applications with request-based scaling down to zero instances. Cloud Run jobs cater specifically to containerized tasks that do not serve HTTP requests and run to completion, supporting execution timeouts up to 24 hours per task. Together, they satisfy both workload requirements with zero server or cluster management.

Step-by-Step Solution

1
Analyze the stateless HTTP REST API requirements.
Identified the need for an HTTP serverless platform with scale-to-zero capabilities and zero server administration.
Cloud Run services automatically handle incoming HTTP requests, auto-scale from zero to thousands of instances based on traffic, and bill strictly for execution time per request.
2
Analyze the background data cleaning workflow requirements.
Identified the need for a serverless container execution model supporting non-HTTP, long-running (4-hour execution duration) batch jobs.
Cloud Run jobs run container images to completion without listening for HTTP requests and support task timeout limits up to 24 hours.
3
Evaluate alternative compute platforms against operational overhead constraints.
Rejected GKE, Compute Engine MIGs, and App Engine Standard Environment.
GKE and MIGs introduce unnecessary cluster/OS operational overhead and baseline infrastructure costs, while App Engine Standard is optimized for web serving rather than long-running containerized batch tasks.

Key Concept

Serverless Compute Selection (Cloud Run Services vs. Cloud Run Jobs)
Estimated Time:2m 0s
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Topics

Selecting and Designing Compute and Application Platforms