Sample Questions from Professional Cloud Architect - Google Cloud (PCA)

Preview a few questions below — answers are revealed when you take the exam.

1. A team needs to migrate a large-scale monolithic application to Google Cloud. The application has high traffic and requires minimal downtime. What approach should they take to ensure a smooth transition with minimal impact on users?

   • Implement a gradual migration strategy using a blue-green deployment model, where the new cloud-based application runs in parallel with the existing on-premises application, and switch traffic gradually.
   • Perform a big-bang migration where the entire application is moved to the cloud in one go, ensuring all dependencies are met before the cut-over.
   • Use a canary release strategy where a small portion of users are directed to the new cloud application while monitoring performance and user feedback before a full rollout.
   • Adopt a lift-and-shift approach to quickly move the application to Google Cloud without refactoring, followed by optimizations post-migration.

2. Consider the situation where a company wants to implement a disaster recovery plan using Google Cloud. Which method solves it best for ensuring data integrity and quick recovery in case of a regional outage?

   • Set up a multi-regional Cloud Storage bucket with versioning enabled and use Cloud Functions to automate data replication across regions.
   • Use Google Cloud's Backup and DR service to create automated backups and replicate them to a different region, ensuring quick recovery.
   • Implement a custom solution using persistent disks and manual snapshots, stored in a different region, to ensure data integrity and recovery.
   • Utilize Google Cloud's Data Loss Prevention (DLP) service to secure sensitive data and ensure it is replicated across regions for disaster recovery.

3. Which option best evaluates the performance of a microservices architecture deployed on Google Kubernetes Engine (GKE)?

   • Analyze the container logs and resource utilization metrics using Google Cloud Operations Suite, and conduct load testing to identify bottlenecks.
   • Rely solely on the default monitoring provided by GKE without additional tools or custom metrics.
   • Use third-party monitoring tools exclusively, ignoring Google Cloud's native monitoring capabilities.
   • Manually review the application code for potential performance issues without using any monitoring or logging tools.

4. How should a professional assess the security posture of a Google Cloud environment that hosts sensitive customer data?

   • Conduct regular security audits, implement Identity and Access Management (IAM) policies, use Cloud Security Scanner, and enable VPC Service Controls.
   • Rely solely on default security settings provided by Google Cloud without any additional configurations or audits.
   • Use only third-party security tools for assessment and ignore Google Cloud's native security features.
   • Manually review all access logs and security configurations without using automated tools or services.

5. What strategy should be applied when designing a scalable and cost-effective data pipeline on Google Cloud for processing large volumes of streaming data?

   • Use Google Cloud Dataflow for stream processing, Cloud Pub/Sub for message ingestion, and BigQuery for analytics, with autoscaling enabled.
   • Implement a custom data pipeline using only Compute Engine instances and manual scaling based on traffic patterns.
   • Rely solely on Google Cloud Storage for data ingestion and processing, without using any stream processing services.
   • Use third-party ETL tools for data processing and ignore Google Cloud's native data services.