GCP Professional Cloud Architect Practice Question
You are the lead architect for a global e-commerce company that relies on a regional Google Kubernetes Engine (GKE) cluster spread across three zones. To increase confidence in the platform's resilience, you need to run a production chaos-engineering experiment that answers the following question: Can the order-service continue to meet its availability SLO if one zone suffers a complete network isolation event? Business leadership insists customer impact must be minimal during the test. Which approach best satisfies these requirements?
Cordon and drain every node in one zone of the regional cluster during peak hours, relying on PodDisruptionBudgets to limit outages.
Spin up a separate staging cluster that mirrors production, shut down an entire zone there, and validate that synthetic checks still pass.
Create a 5 % canary release of the order-service, use a namespace-level NetworkPolicy (or Chaos Mesh NetworkChaos) to block egress from canary pods in one zone, monitor production SLOs, and roll back automatically if thresholds are breached.
Use gcloud commands to randomly kill 30 % of order-service pods across all zones in production, then observe error rates in Cloud Monitoring.
The safest way to test a zonal-failure scenario in production is to limit the blast radius with a canary release. Sending only a small percentage of live traffic to a new canary replica set of the order-service lets you scope the fault-injection to that subset while still exercising real production dependencies. Applying a namespace-scoped NetworkPolicy (or a chaos-engineering sidecar such as Chaos Mesh's NetworkChaos CRD) to cut network egress for pods scheduled in a single zone simulates a full network partition for that zone. Continuous SLO monitoring and an automated rollback guard-rail ensure that, if the experiment degrades customer-visible metrics, the change is rapidly reverted. Simply deleting or draining all nodes in a zone without traffic scoping (distractor answers) risks widespread customer impact. Running the test only in a cloned staging environment (another distractor) does not answer whether the production deployment will meet the SLO under real-world load.
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