How to Answer: "How do you approach capacity planning for peak traffic events?"

“Capacity planning requires a combination of predictive modeling, empirical load testing, and failure isolation. Ahead of Black Friday, where traffic was projected at 10x baseline QPS, I calculated throughput limits across API endpoints and database IOPS. We executed synthetic load tests using Locust to break the system deliberately, identifying a Redis connection pool exhaustion point. We reconfigured connection pooling, pre-scaled Kubernetes pod replicas, warm-started AWS Aurora read replicas, and configured dynamic load shedding to drop non-critical background jobs if latency exceeded 200ms. The event experienced 100% uptime with zero degraded customer transactions.”

“I review historical analytics to estimate anticipated QPS. Then I run load testing scripts against a staging environment that mirrors production. I verify that auto-scaling rules trigger early enough, ensure CDN caching rules cover static assets, and check that database connection limits can support maximum scaled pod instances.”

“I help review load testing results with senior engineers, ensure backend caching headers are configured properly, and double-check that alert thresholds are set before high-traffic marketing events.”