Swiggy Containers & Kubernetes interview questions

PDB governs voluntary disruptions only and does not gate rolling updates at all. Candidates who say 'it keeps N pods alive' have just described the part it does not do.

Priority is one number with three separate effects: scheduling order, preemption victim selection, and eviction order under node pressure. Conflating those is the trap; designing tiers that survive both a full cluster and a hot node is the pass.

Utilization-based scaling is the wrong sensor when work arrives as backlog. The design questions are threshold math against drain rate, reaction-time bounds from polling, and why lag-driven fleets oscillate without generous targets and idempotent consumers.

The gating instinct is right and the cardinality is wrong: init containers make every new replica race the same migration. Here is the once-per-release shape interviewers want, plus the rollback rule that shapes all DDL review.

Exit codes, the previous-container log, and a little-known file at /dev/termination-log that carries a container's last words into kubectl output. Scale triage starts with classification, not with opening pods.

The single most common Kubernetes interview question, and the one where a vague answer is most obvious. Every stage you name is a place the interviewer can ask you to debug.

Asked in nearly every senior Kubernetes loop, and most candidates answer with a feature list. The scoring answer names the three problems it solves and then argues for not adopting it.

Both run pods, so the answer sounds obvious until an interviewer asks what happens when you delete one. The guarantees are narrower and more specific than most candidates think.

Everyone can recite the three. The scoring answer is what each failure causes, and why a badly configured liveness probe is more dangerous than none at all.

The pair everyone configures and few can explain. QoS hints at eviction risk, but usage versus requests and Pod priority determine node-pressure ranking, and CPU and memory behave completely differently when you exceed them.

The question that finds out whether you know Kubernetes networking or just use it. The model has three rules and everything about CNI follows from them.

Almost always a finalizer, and the fast fix is the one that loses data. The safe answer explains what the finalizer is protecting against before removing it.

A practical question with a precise answer, and the size is not the real cost. Pull time on every node, registry storage, and attack surface all scale with it.

The scheduler is behaving correctly, which is the part that confuses people. It optimises placement at admission time and never revisits, so a cluster drifts out of balance by design.

Both solve the same surface problem and they disagree about how. The answer that scores names what each one makes hard, and notices that the GitOps tool you use changes the calculus.

Cilium became the default CNI on major managed platforms for a reason that is mechanical rather than fashionable. The iptables datapath degrades with service count, and eBPF replaces a linear scan with a map lookup.

A specific configuration trap with a clean explanation. Both controllers act on the same signal from opposite directions, and the result oscillates.

Default-deny egress breaks name resolution before it breaks anything else, and every connection times out confusingly. The practitioners are the ones who test with positive AND negative probes and know their CNI's fine print.

The senior tells are level-triggered reconciliation and finalizer semantics. The partition question separates people who have written a controller from people who have installed one.

The friendliest DaemonSet question there is, and it still separates people who have operated one from people who have read about one. The scheduling rules hide the traps.