Databricks Containers & Kubernetes interview questions

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.

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.

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.

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.

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.

Namespaces partition resources, not trust. This question finds the people who know which sysctls are namespaced, which ones change every tenant at once, and when only a second kernel will do.

Multiple schedulers are supported, rarely understood, and dangerous in a specific way. The strong answer names the deadlock that gang scheduling fixes and the capacity race it can open.

Controllers should never poll, yet most home-grown operators do. This question separates people who run controllers at ten-thousand-object scale from those who copied a tutorial: watch semantics, cache behaviour, and which LISTs touch etcd.

SSH plus yum is how outages start. The senior answer replaces nodes from versioned images on a cadence, orchestrates with surge capacity and PDBs, and can explain the kernel-skew problem that makes fleet homogeneity a feature.

Two artifacts hide under 'Kubernetes backup' and they recover different things. Interviewers grade the misses: volume bytes, encryption keys, and everything that never lived in etcd at all.

Tenancy is a dial from policy-based soft isolation to hard cluster boundaries with a virtual-control-plane middle ground. The scored answer picks by blast radius requirements and names what each model still leaves shared.

Driver, container toolkit, device plugin, DCGM and discovery must version-match on every node. The operator reconciles that ladder from one ClusterPolicy CR; MIG, upgrades and host kernels are where it bites.

Changing pod resources historically required recreation, which made vertical autoscaling disruptive for workloads with expensive recovery. In-place resize changes that, with limits worth knowing.

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.

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 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.

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.

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.