Datadog Containers & Kubernetes interview questions

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

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.

Compare the configured Service datapaths and their lookup and update mechanisms. The iptables datapath degrades with service count, and eBPF replaces a linear scan with a map lookup.

Explain per-node eligibility and update behaviour, then verify that a Ready collector is actually delivering logs.

The kubelet is a local reconcile loop with its own event stream, probe workers and eviction brain. The detail worth holding onto is that Ready status can reflect either a reported health failure or missing heartbeats, and those have different causes and different fixes.

Dockershim is gone and the CRI contract decides what a runtime must do. The senior-level part is the shim model: a supervisor process that owns exit codes and stdio so the daemon can die without taking workloads with it.

Repeated full-resource polling can make an operator an expensive API client. 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.

Exec into a distroless image and you get nothing, because there is nothing to exec into. Ephemeral containers inject tooling into a running pod without restarting it, and the security caveats are what interviewers really listen for.

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.

Two cleanup mechanisms hide behind one taint: image garbage collection removes unreferenced images by LRU at threshold crossings, while eviction kills pods. Candidates who conflate them fail this question quietly.

Kubelet resource metrics to metrics-server to aggregated API to HPA controller: every hop adds lag and averaging. Walking the path and naming where it distorts demand separates operators from dashboard readers.

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.

Best-effort objects, one-hour TTL, deduplicated by count, invisible unless shipped out. Events narrate scheduling and lifecycle decisions; treating them as durable records is the expensive mistake.

Route attachment and cross-namespace backend access have different owners. Use route status to find the failing boundary before broadening any permission.

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.

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.

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.