Netflix Incident Response & Production Debugging interview questions

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.

NFS waits can stall shutdown and inflate load average while CPUs stay idle. Check the blocked kernel path before deciding whether signals or mount recovery can release it.

The kill was not random and it was not fair. Learn the scoring function the kernel actually uses, then bend it with oom_score_adj before your monitoring dies during the next leak.

Self-healing unavailability is a timeout arithmetic story. Pull the balancer's removal timestamps, lay them against the GC log, and the pause that ejects the instance will be sitting there in plain sight.

Purges act on exact cache keys, and the edge is full of variants the purge never named. Verify per variant at real POPs, then fix the key model instead of chasing URLs forever.

The automation stopped because it could not decide, which is the correct behaviour and an incomplete design. Somebody has to decide, and the interesting question is what evidence that decision needs.

A health endpoint that returns 200 because the process is running tells you the process is running. Interviewers want to hear where you draw the line between a check that pages and a check that removes traffic.

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.

An idle database with a starved application is the tell. The connections are held, not used. More database capacity does not release leaked connections.

Status codes describe an HTTP failure category. Locate the component that emitted the response, then use its logs to distinguish connection, capacity and application failures.

Three places, and which one depends on how the thing was started. Knowing which logs the runtime collects and which the application writes itself, and that journald may not persist across reboots, saves the first twenty minutes.

A common scenario question, and the one where a list of commands most often stands in for an argument about what each one eliminates. Empty logs is the clue, not the obstacle.

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.

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.

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

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.

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.

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.

Automation can renew a certificate without deploying it correctly. Check the served leaf and chain, the client trust store and the exact verification failure.

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.