Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam!
Prepare and test your skills
Prepare and test your skills
Worked example. The correct answer is already marked and every option is explained below, so there is nothing to select here. To answer questions yourself, start the free trial.
Keep the momentum going with these hand-picked practice scenarios
Want more questions like this?
Get a free certification question every week.
Last updated
An e-commerce company is preparing for a high-traffic flash sale event. The core web service is hosted on Google Kubernetes Engine (GKE) behind a global external Application Load Balancer with backends distributed across multiple Google Cloud regions.
To ensure the system meets a Service Level Objective (SLO) of 99th percentile (p99) latency below 200 ms, the engineering team needs to design a distributed load testing framework to simulate massive, concurrent user traffic originating from multiple geographic regions while verifying Horizontal Pod Autoscaler (HPA) and cluster autoscaling performance.
How should the team architect this load testing solution on Google Cloud?
Configure Google Cloud Armor rate limiting rules on the load balancer to artificially inject synthetic latency and dropped packets to simulate heavy traffic load.
Deploy a regional internal Application Load Balancer with traffic splitting to route 100% of production traffic to a staging GKE cluster during off-peak hours.
Deploy a standalone JMeter instance on a single high-memory, high-CPU Compute Engine VM in the primary region to run threaded test scripts directly against the internal Pod IP addresses.
Deploy a distributed Locust load testing framework across GKE clusters in multiple target regions, with master nodes aggregating metrics and worker pods generating parallel traffic against the global external Application Load Balancer endpoint.
Configure Google Cloud Armor rate limiting rules on the load balancer to artificially inject synthetic latency and dropped packets to simulate heavy traffic load.
Deploy a regional internal Application Load Balancer with traffic splitting to route 100% of production traffic to a staging GKE cluster during off-peak hours.
Deploy a standalone JMeter instance on a single high-memory, high-CPU Compute Engine VM in the primary region to run threaded test scripts directly against the internal Pod IP addresses.
Deploy a distributed Locust load testing framework across GKE clusters in multiple target regions, with master nodes aggregating metrics and worker pods generating parallel traffic against the global external Application Load Balancer endpoint.
Locust is an open-source, distributed load testing framework that allows engineers to define user behavior in standard Python code. When deployed on Google Kubernetes Engine (GKE), Locust operates in a coordinator-worker (master-worker) topology. The master pod coordinates test execution, collects metrics, and provides a centralized web UI/API for reporting, while multiple worker pods distributed across nodes scale out horizontally to generate high volumes of concurrent HTTP/HTTPS requests.
StudyCerts
StudyCerts