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Browse 8 real-world technical and behavioral interview questions about Kubernetes. Review scenarios, edge cases, and architectural best practices.
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.
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.
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.
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.
An in-depth look at designing a robust Kubernetes orchestration layer to optimise GPU utilisation across diverse, conflicting machine learning workloads.
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.
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.
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.