GCP Professional Cloud Architect Practice Question
A fintech company operates a multi-zone Google Kubernetes Engine (GKE) cluster that processes real-time payment transactions. Executives want proof that the platform can survive an unexpected network outage in an entire zone without customer impact, but they do not want to wait for a real incident. Which approach most directly achieves the goal of building confidence in the system's ability to withstand turbulent production conditions before actual failures occur?
Run controlled chaos engineering experiments that periodically disrupt a full zone's network connectivity in the live GKE cluster and monitor SLO compliance.
Carry out scheduled disaster-recovery failover tests only in a dedicated staging environment that mirrors production.
Add comprehensive readiness and liveness probes so Kubernetes restarts any containers that become unresponsive.
Enable Horizontal Pod Autoscaling (HPA) to add replicas automatically when latency increases.
Chaos engineering purposely injects realistic failures-such as zone-level network partitions, pod crashes, or node shutdowns-against the live production environment while carefully monitoring service-level objectives (SLOs). By observing that the application continues to meet availability and latency targets during these controlled experiments, the team gains empirical evidence that the system can tolerate real-world disruptions. Autoscaling, health probes, or isolated disaster-recovery drills improve reliability, but they do not actively validate resilience by simulating failures in production.
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