DevOps Interview Help — AI for Kubernetes, CI/CD, Cloud & Incidents

1. Linux, networking & scripting

Process and memory inspection, file permissions, signals, DNS and the TCP handshake, and a Bash/Python scripting task. Common: "a server is at 100% CPU — debug it" (top, ps, strace, logs). The AI gives the diagnostic command sequence, not just the answer.

2. Containers & Kubernetes

Docker layers and image size, then K8s: Pods, Deployments, Services, Ingress, ConfigMaps/Secrets, probes, and "why is my pod CrashLoopBackOff?" The AI surfaces the debug path (kubectl describe, events, logs, resource limits).

3. CI/CD & infrastructure as code

Pipeline design (build → test → scan → deploy), blue/green vs canary, rollbacks, and Terraform state, modules, and drift. The AI lays out a safe deploy strategy and the trade-offs interviewers probe.

4. Cloud system design

"Design a highly available, auto-scaling web service on AWS." Graded on load balancing, multi-AZ, autoscaling, caching, observability, and cost. The AI provides the standard reference architecture so you cover availability, scaling, and monitoring.

5. Incident / on-call scenario

"Latency just spiked in prod — walk me through it." Graded on a calm, structured response: detect, triage, mitigate, root-cause, follow-up. The AI prompts the incident framework so you don't freeze.

1. "Walk me through a CI/CD pipeline you'd design from scratch."

Name the ordered stages — commit → build → unit/integration test → security scan → package an immutable artifact → deploy to staging → gated promotion to production — and stress that a failed stage halts promotion. Mention version-pinned artifacts and automated rollback so the interviewer hears safety, not just speed.

2. "What's the difference between containers and virtual machines?"

VMs virtualize hardware and each ships a full guest OS; containers share the host kernel and isolate at the process level, so they're lighter and start in milliseconds. Tie it back to why that matters: density, fast scaling, and reproducible images.

3. "How does Docker image layering work, and how do you keep images small?"

Each instruction in a Dockerfile adds a cached, read-only layer; ordering stable steps first maximizes cache hits. For size, reach for multi-stage builds, a slim base image, and combining RUN steps so intermediate artifacts don't persist in a layer.

4. "Explain the core Kubernetes objects."

A Pod is the smallest deployable unit; a Deployment manages a replica set and rolling updates; a Service gives stable networking and load-balancing to a set of pods; an Ingress routes external HTTP(S) traffic in. Add ConfigMap and Secret for configuration to show depth.

5. "Why is Infrastructure as Code valuable, and what does idempotency mean?"

IaC (e.g. Terraform) makes infrastructure versioned, reviewable, and repeatable instead of click-ops. Idempotency means applying the same configuration repeatedly converges to the same end state — terraform plan before apply, with remote state and locking to prevent drift and races.

6. "Compare blue-green, canary, and rolling deployments."

Rolling replaces instances in batches; blue-green keeps two full environments and flips traffic for an instant rollback; canary shifts a small percentage of traffic first and watches metrics before widening. Pick based on rollback speed, cost, and acceptable blast radius.

7. "What are the three pillars of observability, and how do you respond to an incident?"

Metrics (aggregate trends and alerting), logs (discrete events for forensics), and traces (a request's path across services). For incidents, walk the lifecycle — detect, triage, mitigate, root-cause, blameless postmortem — and mention on-call rotation and runbooks.