Swiggy Containers & Kubernetes interview questions

A pod is a shared network and storage boundary that happens to contain containers. Once you can say what the containers inside one share, sidecars and init containers stop needing separate explanations.

Three states, three completely different causes, and one command that tells you which you are looking at. The state name says whether the problem is the scheduler, the registry or your own process.

The writable layer is part of the container, not the image, and it dies with the container. Everything about volumes follows from knowing which of those two a file is in.

PDB governs voluntary disruptions only and does not gate rolling updates at all. Describing it as 'it keeps N pods alive' states the part it does not do, since it constrains voluntary eviction and not the machine that fails.

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, 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.

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.

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.

Service types choose how an application is exposed. Follow the actual traffic path: cloud controllers can target nodes or pods, and internal exposure differs from public exposure.

A Service moves connections and an Ingress reads requests. The gap between those two sentences is hostname routing, path routing and TLS, and it is also why an Ingress object alone does nothing.

Follow a Deployment change through the API server, controllers, scheduler and kubelet. Each handoff gives you a different place to investigate when a pod never starts.

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.

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

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.

Build instructions depend on earlier inputs, so changing source can invalidate a dependency installation placed after it. Ordering the Dockerfile by how often things change is the whole optimisation, and multi-stage is how the compiler stops shipping to production.

Evaluate identity, traffic policy and telemetry against measured proxy overhead and operating responsibilities, then test the authorization boundary.

Trace sandbox setup, address allocation and a cross-node packet, then distinguish pod reachability from Service translation.

A VM virtualises hardware and boots its own kernel. A container is a process on the host kernel with its view of the world restricted. Everything else, including the security answer, follows from that one difference.

A namespace scopes names, permissions and quotas. It does not scope the kernel or the network by default, and the gap between what people assume it isolates and what it does is where the trouble starts.

Selectors match object labels; owner references record controller relationships. Keeping those mechanisms separate explains how a Service can select the wrong pods without owning them.

A ReplicaSet keeps a count. A Deployment manages a sequence of ReplicaSets, and that is the entire difference: it is what turns a change into a rollout you can pause and undo.