GCP Professional Cloud Architect Practice Question
Your company operates a business-critical e-commerce platform consisting of over 60 containerised microservices running on two regional GKE clusters behind a global HTTP(S) Load Balancer. After a defect in the inventory service triggered a cascading failure during peak traffic, leadership asks how to prove the platform can survive similar issues before the holiday season. Redundancy, health checks, circuit breakers, and autoscaling are already in place. Which additional action best addresses this request, and why?
Add 50 % more node capacity to each GKE cluster so unexpected load spikes are less likely to exhaust resources and cascade across services.
Enable Cloud Armor adaptive protection on the load balancer to block anomalous traffic, removing the need to test internal service failures.
Switch from rolling updates to blue/green deployments so only fully validated releases reach production, eliminating the requirement for fault-injection testing.
Schedule recurring chaos experiments in production that randomly terminate pods and inject latency while you monitor SLO dashboards to confirm the platform absorbs the faults without breaching error budgets.
The remaining gap is not additional capacity or different release mechanics, but empirical evidence that the system behaves as expected when components fail. Chaos engineering supplies that evidence by deliberately introducing realistic faults-such as random pod terminations or network latency-in the actual production environment while observability tools verify that SLOs continue to be met. Over-provisioning or shielding traffic may reduce some risks but does not validate service-level fault tolerance. Changing to blue/green deployments limits release risk but still leaves unknowns about how the live microservices interact under failure. Only controlled chaos experiments directly demonstrate resilience to cascading faults in complex, distributed systems.
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What are chaos experiments in production?
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How does fault tolerance differ from redundancy?
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How do Service Level Objectives (SLOs) and error budgets work?
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