Professional-Cloud-Architect 試験問題を無料オンラインアクセス

Your company has a stateless web API that performs scientific calculations. The web API runs on a single Google Kubernetes Engine (GKE) cluster. The cluster is currently deployed in us-central1. Your company has expanded to offer your API to customers in Asia. You want to reduce the latency for the users in Asia. What should you do?

• A.Use a global HTTP(s) load balancer with Cloud CDN enabled
• B.Create a second GKE cluster in asia-southeast1, and use kubemci to create a global HTTP(s) load balancer
• C.Increase the memory and CPU allocated to the application in the cluster
• D.Create a second GKE cluster in asia-southeast1, and expose both API's using a Service of type Load Balancer. Add the public Ips to the Cloud DNS zone

Your company is running AI/ML workloads on graphics processing units (GPUs) within Google Kubernetes Engine (GKE). These workloads are deployed across production and development projects, with the production environment being business critical and operating 24/7. You need to ensure that the GPU capacity is always available during scaling events, without being limited by overall GPU availability. You also need the ability to shift unused GPU capacity from production to development projects as needed. You want to follow Google-recommended best practices. What should you do?

• A.Create a shared reservation in the production project, and then add the development project as a consumer project to move capacity when needed.
• B.Create a reservation in the production project, and then create a separate reservation in the development project to secure capacity.
• C.Create an owner project with shared reservations, and then configure the production project and development project as consumer projects to consume capacity.
• D.Create a reservation in the production project, and then transfer the reservation to the development project when needed.

Your product learn is building a critical, customer-facing application on Google Cloud The development team wants to use Spanner for their database to take advantage of its horizontal scalability and low operational overhead However, the FinOps team is concerned about the direct monthly cost of Spanner and proposed using a self-managed PostgreSQL database on Compute Engine VMs instead. You need to resolve this conflict and ensure the project moves forward with an architecturally sound database choice that balances technical requirements with financial constraints. What should you do?

• A.Develop a total cost of ownership (TCO) analysis that includes operational overhead, and present it m a workshop to facilitate a decision.
• B.Suggest using Cloud SQL for PostgreSQL as a compromise to get a managed service at a lower cost than Spanner.
• C.Provide the development team with a reference architecture for deploying a highly available PostgreSQL cluster on a regional managed instance group (MIG).
• D.Cite the reliability and performance optimization pillars of the Google Cloud Well-Architected Framework to formally justify the use of Spanner.

For this question, refer to the TerramEarth case study.
TerramEarth plans to connect all 20 million vehicles in the field to the cloud. This increases the volume to 20 million 600 byte records a second for 40 TB an hour. How should you design the data ingestion?

• A.Vehicles write data directly to GCS.
• B.Vehicles continue to write data using the existing system (FTP).
• C.Vehicles stream data directly to Google BigQuery.
• D.Vehicles write data directly to Google Cloud Pub/Sub.

For this question, refer to the Mountkirk Games case study.
Mountkirk Games wants to set up a real-time analytics platform for their new game. The new platform must meet their technical requirements. Which combination of Google technologies will meet all of their requirements?

• A.Container Engine, Cloud Pub/Sub, and Cloud SQL
• B.Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow
• C.Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow
• D.Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery
• E.Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc