Professional-Cloud-Architect Dumps with Practice Exam Questions Answers

According to Google Cloud's Architecture Framework for Performance Excellence, the most effective way to reduce latency for a global user base is to move content closer to the users.

Option C (Cloud CDN) is the primary recommendation for static assets. By leveraging Cloud CDN with a Global External HTTP(S) Load Balancer, static content (images, CSS, JS) is cached at Google's edge points of presence (PoPs) globally. When a user in Europe or Asia requests an image, it is served from a nearby edge cache rather than traveling back to us-central1, drastically reducing the Time to First Byte (TTFB).

Option D addresses the application logic (frontend) latency. By deploying Managed Instance Groups (MIGs) in Europe and Asia, the initial request processing happens locally. While the dynamic data might still rely on a central database, the frontend presentation and initial connection handshake are localized.

Option A focuses on database read latency, which is helpful but does not solve the "slow loading static content" issue identified in the feedback. Option B is incorrect because a Regional Network Load Balancer cannot terminate SSL at the edge or support Cloud CDN. Vertically scaling (undisclosed option) is a "brute force" approach that does not solve the physical distance/speed-of-light issue inherent in global networking.

https://cloud.google.com/compute/docs/import/importing-virtual-disks

The most direct and impactful way to reduce costs for predictable, time-bound workloads is to stop resources when they are not in use. According to the Google Cloud Architecture Framework: Cost Optimization, Compute Engine instances are billed per second while running. By scheduling instances to run only from 9:00 AM to 6:00 PM on weekdays, the instances are active for only 45 hours out of a 168-hour week. This results in a cost reduction of approximately 73%.

While Committed Use Discounts (CUDs) (Option B) provide significant savings (up to 57-70%), they require paying for the resources 24/7 for the duration of the commitment. Even with a 70% discount, paying for 24/7 usage is more expensive than paying full price for only 27% of the week. Autoscaling (Option C) is generally more effective for handling fluctuating traffic loads rather than developer workstations, which are often stateful or individual-specific. Instance Scheduling is a native Compute Engine feature that allows administrators to define start and stop operations automatically, ensuring that productivity is maintained by having the machines ready when developers start their day, while maximizing savings during off-hours.

According to the Google Cloud SRE (Site Reliability Engineering) handbook and Vertex AI Monitoring documentation, Latency is the fundamental metric used to measure the time it takes to service a request. In the context of "real-time recommendations" for Cymbal Retail, understanding the overall duration—from the moment the app requests a feature to the moment it receives the response—is critical for maintaining the user experience.

In Vertex AI Feature Store, latency is typically measured at various percentiles (e.g., p50, p95, and p99) to account for outliers. High latency in the online serving path directly impacts the speed of the recommendation engine.

While Online serving throughput (Option B) and Queries per second (QPS) (Option D) are important metrics, they measure capacity and volume (how many requests the system can handle simultaneously) rather than the duration of an individual request. Request size (Option C) measures the payload volume, which can influence latency but does not define the time duration itself. To address the specific requirement of measuring "overall duration," monitoring request latency within Cloud Monitoring is the standard architectural practice.

https://cloud.google.com/blog/products/management-tools/automate-your-response-to-a-cloud-logging-event