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Explanation:

HipLocal's current architecture is not designed for global scalability or high availability. The correct approach involves leveraging Google Cloud's managed services to ensure scalability, reliability, and reduced management overhead. Option C is the best choice because it utilizes Memorystore for efficient session management, CloudSQL for scalable and managed database services, and managed instance groups with a load balancer for automatic scaling and distribution of traffic. This setup aligns with Google's recommended practices for cloud computing, ensuring the application can handle a large increase in users while maintaining performance and availability. Options A, B, and D either do not fully address the scalability requirements or are not suitable for dynamic applications like HipLocal.

Explanation:

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How should HipLocal redesign their architecture to ensure the application scales to support a significant increase in users while meeting business and technical requirements?

Key considerations:

Replace the single-instance MySQL database with a globally scalable solution (e.g., Cloud Spanner or sharded Cloud SQL).

Migrate APIs from Compute Engine to a managed service like Google Kubernetes Engine (GKE) or Cloud Run for autoscaling.

Implement distributed logging (Cloud Logging) and monitoring (Cloud Monitoring) with actionable alerts.

Adopt CI/CD pipelines (e.g., Cloud Build) to automate deployments and reduce manual intervention.

Use global load balancing (Cloud Load Balancing) and CDN (Cloud CDN) to ensure low-latency access across regions.

Enforce SLIs/SLOs and regional compliance (e.g., data residency via Firestore or Spanner configurations).

Integrate authentication/authorization (Identity-Aware Proxy, Firebase Auth) and secure communication (VPC peering/TLS).

Leverage Pub/Sub or Eventarc for asynchronous processing to handle spikes in demand.

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Last updated: December 24, 2025 at 14:03

Use Google Kubernetes Engine (GKE) to run the application as a microservice. Run the MySQL database on a dedicated GKE node.

21.6%

Use multiple Compute Engine instances to run MySQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.

9.5%