Flipkart 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.

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.

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.

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

One saturated core on a proxy is rarely about raw traffic volume. It is a parallelism failure with three usual suspects, and the per-thread view names the culprit in minutes.

Same-node good, cross-node bad is not a random clue, it is nearly a diagnosis. One short decision tree separates overlay reachability, encapsulation blocks, MTU and policy.

The reflex is rollback and it is usually right. There is exactly one class of failure where undo makes things worse, and knowing it is the entire interview.

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.

The backup is doing its job and the database is paying for it. Attribution first, then enforcement that actually binds on modern kernels, because priority support depends on the active kernel and IO scheduler.

Spikes that march in step with BGSAVE are a page-size story, not a network story. Check actual huge-page usage, fork duration and copy-on-write pressure on the deployed kernel.

A public registry throttles by source IP and your whole fleet shares one. Why fifty nodes count as a single anonymous user, and authentication and caching changes that reduce upstream demand.

Mixed resolution after a transfer is not propagation voodoo. Compare the parent delegation, authoritative answers and validation results. NXDOMAIN and SERVFAIL are clues; neither names a unique cause.

Stop the source before tuning anything: kill the runner, revoke its credentials, shed test-shaped traffic at the edge, protect checkout. Forensics and guardrails only after customers stop feeling it.

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.

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.

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.