20+ practice questions focused on Designing highly scalable, available, and reliable cloud-native applications — one of the most tested topics on the Google Professional Cloud Developer exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Designing highly scalable, available, and reliable cloud-native applications PracticeWhich three factors should be considered when choosing a regional vs. multi-regional deployment for a globally distributed application?
Explanation: When choosing between regional and multi-regional deployment for a globally distributed application, three key factors should be considered: data residency requirements, cost of data transfer, and latency for users. Data residency requirements (A) mandate that certain data must remain within specific geographic boundaries due to legal or regulatory obligations (e.g., GDPR, HIPAA). Choosing a regional deployment ensures data stays within a single region, while multi-regional deployment may require complex data replication and compliance with multiple jurisdictions. Cost of data transfer (B) is a significant factor because multi-regional deployments involve frequent data replication and cross-region traffic, which can incur higher costs compared to a single regional deployment. Latency for users (E) is critical for globally distributed applications; multi-regional deployment allows placing resources closer to users worldwide, reducing latency, while regional deployment may increase latency for remote users. Options C (single region compliance) is not a distinct factor because compliance is inherently tied to data residency requirements. Option D (replication lag) is a technical consideration but is not among the primary factors for choosing regional vs. multi-regional deployment at the architectural level.
A company is designing a cloud-native application on Google Cloud that requires low-latency access to a global user base. The application serves static content and dynamic APIs. Which strategy best minimizes latency while maintaining high availability?
Explanation: It combines Cloud CDN for caching static content at edge locations worldwide, reducing latency for static assets, and deploys the dynamic API across multiple regions with global load balancing (using Google Cloud's global external HTTP(S) load balancer) to route users to the nearest healthy backend, minimizing latency for dynamic requests while ensuring high availability through regional redundancy.
A team is migrating a monolithic application to a microservices architecture on Google Kubernetes Engine (GKE). They want to ensure that failures in one microservice do not cascade to others. Which design pattern should they implement?
Explanation: The circuit breaker pattern is the correct choice because it prevents cascading failures by monitoring inter-service calls and opening the circuit when failures exceed a threshold, allowing the system to fail fast and recover gracefully. In a GKE-based microservices architecture, this pattern is typically implemented using libraries like Resilience4j or Istio's circuit breaker, which can be configured to trip after a certain number of consecutive failures, thus protecting downstream services from being overwhelmed.
A company running a high-traffic e-commerce platform on Google Cloud experiences occasional data loss in their Cloud SQL database during failover events. The database is configured with a failover replica in a different zone. What is the most likely cause of the data loss?
Explanation: Cloud SQL uses synchronous replication for failover replicas by default, ensuring that transactions are committed on both the primary and the replica before acknowledging the write. If asynchronous replication is configured, the replica may lag behind the primary, and during a failover, any transactions not yet replicated are lost. This is the most likely cause of data loss during failover events.
An organization wants to design a serverless data processing pipeline that is highly available and can automatically scale based on the number of incoming requests. The pipeline processes JSON messages from a Cloud Pub/Sub topic and writes results to BigQuery. Which service should be used as the compute component?
Explanation: Cloud Run is the correct compute component because it is a fully managed serverless platform that automatically scales from zero based on incoming HTTP requests, supports event-driven processing via Pub/Sub push subscriptions, and integrates natively with BigQuery. It provides high availability by default across zones and can handle burst traffic without provisioning overhead, making it ideal for a serverless pipeline that processes JSON messages and writes results to BigQuery.
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Practice all Designing highly scalable, available, and reliable cloud-native applications questions1. Baseline your knowledge
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2. Review every explanation
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3. Focus on exam traps
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4. Reach 80% consistently
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