Ngân hàng đề — Google Professional Cloud Architect

Your company is restructuring its operations and you've been tasked with reorganizing your Google Cloud resources to match the new business units. You currently have all resources in one project. Your company is divided into several departments, each containing multiple teams. You need to ensure that billing reports can be generated per department, and resources can be isolated per team. What is the most effective way to reorganize your resources?

You are the lead cloud architect for a global company that is migrating its data processing workloads to Google Cloud Platform (GCP). The company's primary requirements include scalability, cost-effectiveness, and the ability to process and analyze data in real-time. You need to choose the most appropriate GCP service to meet these requirements. Which of the following would you recommend?

Your team is building a real-time multiplayer game backend on Google Cloud that must ensure minimal latency for players located across North America, Europe, and Asia. The backend uses a RESTful API, real-time state synchronization over WebSockets, and a Cloud Spanner database. What architectural design will best help reduce latency for players in all regions?

For this question, refer to the Cymbal Retail case study.

https://services.google.com/fh/files/misc/v6.1_pca_cymbal_retail_case_study_english.pdf

Cymbal wants to automate product discovery by allowing customers to search for products using natural language queries such as “lightweight running shoes for trail running under $150”. The solution must scale with traffic spikes, integrate with Cymbal’s existing Kubernetes-based microservices, and reduce reliance on manual catalog tagging. Cymbal also wants to minimize operational overhead and avoid managing custom ML infrastructure. Which architecture best meets Cymbal’s requirements for automated, natural language–driven product discovery?

Your company is operating a multi-tier web application on Google Cloud. The frontend servers should be globally available and scale automatically to handle traffic, while the backend servers should be accessed only by the frontend servers and internal systems. The data transfer between backend and frontend servers needs to be encrypted. As a cloud architect, how should you design your network to fulfill these requirements?

You are a cloud architect at a multinational corporation that has recently decided to move its infrastructure to Google Cloud Platform. The company has several departments globally, with each department having multiple teams. You need to design a resource hierarchy that allows you to apply company-wide policies, segregate resources at the department level, and isolate resources at the team level. What would be the best way to set up your GCP resource hierarchy?

Personally Identifiable Information (PII) and sensitive information about your company's customers should be stored securely in Cloud Storage. Several people from your company's compliance department need access to some of this information. As a cloud architect, what should you do to follow Google's best practices?

FinServCorp, a financial services company, is migrating its trading platform to Google Cloud. The platform processes real-time financial transactions, and the company must comply with stringent financial regulations that require comprehensive logging, monitoring, and auditing of all transactions. The platform will be deployed using a microservices architecture on Google Kubernetes Engine (GKE) with multiple clusters across regions to ensure high availability. The company also needs to ensure that all logs are stored securely and meet the retention requirements specified by financial regulations. What is the most appropriate solution for ensuring compliance with financial regulations while providing full observability of the trading platform?

Production Compute Engine workload is running in a small subnet, which can be expanded. The recent spike in traffic has caused problems, but there are no free IP addresses for Managed Instances Group to autoscale. What should you do?

For this question, refer to the Altostrat Media case study.

https://services.google.com/fh/files/misc/v6.1_pca_altostrat_media_case_study_english.pdf 

Altostrat plans to burst compute-intensive media processing workloads (e.g., video analysis and metadata enrichment) from its on-premises Kubernetes cluster into Google Cloud during peak demand. These workloads must scale quickly, maintain consistent networking and security policies, and integrate with existing GKE-based services. What is the most appropriate architecture to support elastic scaling across on-premises and cloud Kubernetes environments while meeting Altostrat’s performance and governance requirements?