Top 8 Kubernetes Interview Questions
Browse 8 real-world technical and behavioral interview questions about Kubernetes. Review scenarios, edge cases, and architectural best practices.
Pods are being evicted during node pressure even though your CPU dashboards look fine. Where do you look, and what would you change so it stops?
CPU is compressible, so the kubelet throttles it rather than evicting for it. Eviction comes from memory, ephemeral storage or inodes - and which pods die is decided by QoS class, not by who caused the pressure. Check node conditions and eviction thresholds first, then fix the requests and limits so critical workloads are Guaranteed rather than BestEffort.
A pod is stuck in CrashLoopBackOff. How do you debug it?
Kubernetes CrashLoopBackOff debugging starts with the previous container logs, exit code and pod events. Then separate application crashes from liveness probe kills, bad configuration, missing dependencies and OOMKilled restarts.
Every rolling update drops a small number of requests. Where do they go?
Removing a pod from its EndpointSlice and sending it SIGTERM happen concurrently, and the data-plane update takes time to propagate, so requests keep arriving at a pod that has already begun shutting down. The fix is a preStop delay plus an application that finishes in-flight work before exiting.
Design an autoscaling scheme for a self-hosted CI runner fleet. What are you actually trading off, and how do you decide the numbers?
Runner autoscaling trades idle compute cost against queue time, and the two levers that decide the trade are a warm pool sized to your usual concurrency and a scale-down delay long enough to absorb the next job without a fresh cold start.
How would you architect a Kubernetes cluster to manage a heterogeneous fleet of GPUs simultaneously running massive distributed training jobs and latency-sensitive inference services without catastrophic degradation?
An in-depth look at designing a robust Kubernetes orchestration layer to optimise GPU utilisation across diverse, conflicting machine learning workloads.
Would you use iptables or eBPF for network policy enforcement in a massive multi-tenant Kubernetes cluster, and what are the operational trade-offs?
eBPF-based Kubernetes network policy can enforce hard multi-tenancy with faster identity-aware checks than long iptables chains, but it demands verifier-safe rollouts, fail-closed controls, and strong policy observability.
A Kubernetes cluster admin account has been compromised for 45 minutes. Do you clean the cluster or burn it to the ground, and why?
Evaluate the candidate's crisis management and technical recovery skills in a high-stakes scenario involving a total compromise of Kubernetes administrative control. Use this security answer to show the decision, trade-off, and evidence rather than a memorised definition. It also connects incident response to the point an interviewer is testing.
Your worker nodes take operating system and kernel updates automatically. What is the risk, and how would you make node-level change safe?
A node image change is a release of the layer underneath Kubernetes, so the control plane cannot stage it, roll it back or reliably report on it. Treat node pools as rings, replace nodes rather than patching them in place, and get the health signal from outside the cluster. It also connects node lifecycle to the point an interviewer is testing.