IntermediateBEHAVIORAL
Tell me about a time you were on call for a production incident related to a Kubernetes or containerized workload. How did you diagnose the issue end-to-end, what tools and signals (logs, metrics, traces) did you rely on, and what long-term improvements did you drive afterward?
DevOps Engineer
General

Sample Answer

I was on call one night when our main API on EKS started throwing 5xxs and latency spiked from ~80ms to over 1s for about 20% of traffic. First thing, I checked our Grafana dashboards: pod CPU was fine, but HPA events showed frequent scale‑ups with long pending pods. In CloudWatch and kube-events, I saw image pull timeouts and nodes hitting disk pressure. I tailed logs with kubectl and Loki and confirmed requests were queueing, not failing in app code. Using X-Ray traces, I saw most time spent before the app even received traffic. We mitigated by cordoning the bad nodes, manually scaling the node group, and pre-pulling critical images. Post‑incident, I worked with the team to add a dedicated image cache node pool, lowered image size by ~40%, and added SLO-based alerts on pod pending time. Similar incidents dropped to zero over the next six months.

Keywords

Used metrics (Grafana, HPA, node status) to quickly narrow the issue to infrastructureCorrelated logs, events, and X-Ray traces to confirm the bottleneck was image pulls and node disk pressureImplemented immediate mitigation (cordon nodes, scale cluster, pre-pull images)Drove long-term fixes: image optimization, cache node pool, SLO-based alerts; eliminated repeat incidents
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