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Answer: Use Memorystore to store session information and CloudSQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.

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.

Author: LeetQuiz Editorial Team

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Author: LeetQuiz Editorial Team

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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.

15.9%

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.

11.4%

Use Memorystore to store session information and CloudSQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.

68.2%

Use a Cloud Storage bucket to serve the application as a static website, and use another Cloud Storage bucket to store user state information.