GCP Professional Cloud Architect Practice Question

Your e-commerce platform runs on GKE in two regions and uses Anthos Service Mesh (ASM) for traffic management. Leadership wants evidence that the checkout microservice will continue to meet its 95th-percentile latency SLO when its dependency on the inventory service experiences a 500 ms delay during peak Black Friday traffic. You must test in production with minimal customer impact, gather metrics to trigger an automatic rollback, and avoid modifying application code. Which approach best fulfils these requirements?

  • Create an ASM virtual service rule that injects a 500 ms delay on 2 % of checkout-to-inventory calls, monitor the checkout service's Cloud Monitoring SLO with a burn-rate alert, and disable the rule automatically if the alert fires.

  • Adjust Cloud Load Balancing backend time-outs to 500 ms for the inventory service during the sale and review Cloud Logging for error spikes.

  • Release a new inventory service version that sleeps 500 ms before responding, perform a blue/green cut-over, and inspect Cloud Profiler for CPU anomalies.

  • Build a dedicated staging cluster, run a JMeter stress test against checkout from Cloud Build, and analyse Cloud Trace spans for latency regressions.

GCP Professional Cloud Architect
Ensuring solution and operations excellence
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