Google Incident Response & Production Debugging interview questions

The most-asked scenario question in DevOps interviews, and the one where candidates most often list commands instead of reasoning. Empty logs is the clue, not the obstacle.

A senior favourite, because the naive answer is to look at averages and the average is exactly what is hiding the problem. Tail latency has a short list of causes and they are all structural.

Tests whether you know that Kubernetes waits, and for how long. The default timings surprise most candidates, and they explain why a node failure hurts longer than people expect.

Both can be right, which is the whole point. Retransmissions mean a segment was not acknowledged, and the network is only one of the places that can happen.

Low utilisation with terrible tail latency is not a contradiction. It is the CFS quota doing exactly what it was configured to do. Most candidates have never read nr_throttled.

A two-second dependency should cost you latency, not three services. The mechanics are arithmetic: Little's law plus retry amplification. The fix is a budget, not a ban.

Repeated action items are a measurable defect with a mechanical cause. Candidates who answer 'culture' without a mechanism are describing the symptom back to you.

One disk metric explaining a whole cluster's weirdness is the point of this question. Candidates who restart etcd first turn a slow cluster into a down one.

Gradual drift produces no errors on the host that has it, only weirdness everywhere else. Sweep first, histogram second, repair third: the distribution shape names the culprit before you log into anything.

Slow leaks hide inside restart cycles and percentage thresholds. Plot weeks of absolute memory against deploys, prove it with profiler deltas, and alert on predicted time-to-limit instead.

Fork failures can reflect PID limits or memory allocation failure even when host RAM looks available. Find which ceiling broke, cap PIDs per cgroup so one team's leak costs one pod, and fix the unbounded spawn.

The pods are Pending, the nodes are Ready, and the GPUs have vanished from allocatable. Something changed on the node under a fleet that nobody considered part of the deploy surface.

The gentlest scheduling question there is, and it still separates people who read the events from people who guess. Check whether the pod has a node before choosing between scheduling and startup diagnosis.

The pods are Running, the dashboard is green, and users get errors. The gap between Running and receiving traffic is where this question lives, and it has a precise answer.

Intermittent DNS is the most-reported and least-understood Kubernetes failure. There are three well-known causes and each leaves a different fingerprint.

A deceptively deep question, because there are at least six places a version can get stuck and they span the pipeline, the cluster and the edge. Work the path in order.

Tests the mitigate-before-diagnose reflex under time pressure, plus whether you know the two ways a disk can be full and the trap that makes freed space not come back.

The classic gap between what the backend measures and what the user experiences. A 504 is emitted by something in front of the backend, which is exactly the part your service dashboards do not cover.