An online multiplayer gaming platform is architecting its global matchmaking and player profile backend on Google Cloud. The architecture must achieve high availability across regional outages with a Recovery Point Objective (RPO) of 0 and a Recovery Time Objective (RTO) of less than 1 minute for transactional player state. Additionally, the compute tier handles persistent WebSocket connections and I/O-intensive queue processing, which does not correlate directly with CPU usage. Which TWO architectural decisions should the Cloud Architect implement to satisfy these technical requirements? (Select 2 answers)
- Deploy a multi-region Cloud Spanner instance to provide globally distributed strongly consistent transactions with zero RPO across regional failures.Cevap
- BDeploy a regional Cloud SQL for PostgreSQL instance with asynchronous cross-region read replicas and manual failover procedures.
- Configure Kubernetes Horizontal Pod Autoscaler (HPA) using custom metrics for active WebSocket connections and queue length rather than default CPU utilization.Cevap
- DConfigure Kubernetes Horizontal Pod Autoscaler (HPA) based exclusively on average CPU utilization target of 80% across pods.
- EConnect the compute VPC to a central hub VPC using VPC Network Peering and rely on transitive routing to access external third-party services.
Cevap
The architect should deploy a multi-region Cloud Spanner instance for zero-RPO transactional data and configure Kubernetes Horizontal Pod Autoscaler using custom metrics for active connections and queue length.
Deploying a multi-region Cloud Spanner instance guarantees synchronous multi-region replication with zero data loss (RPO = 0) and automatic sub-minute failover. Additionally, configuring HPA with custom application metrics (WebSocket connections and queue depth) ensures the compute tier scales appropriately for I/O-bound workloads that do not correlate with CPU consumption.
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Anahtar Kavram
Designing multi-region database architectures for zero RPO/RTO and autoscaling I/O-bound compute workloads using custom metrics.