DevOps engineer remains one of the most-searched tech roles, and interviews in 2026 increasingly mix classic infrastructure questions with AI-era topics like GPU workloads and LLM-app deployment. These questions cover what interviewers actually ask, with pointers on what a strong answer includes.
1.Walk me through what happens when you push a commit and it ends up in production.
What a strong answer covers: The classic pipeline question. Cover: trigger (webhook), CI stages (lint, unit tests, build, artifact/image push), environment promotion, deployment strategy (rolling, blue-green, canary), and post-deploy verification (health checks, smoke tests, automatic rollback). Name the tools you actually used and one failure you caught in each stage — specificity separates practitioners from readers.
2.A deployment caused an outage. Walk me through your incident response.
What a strong answer covers: Structure: detect (alerting fired or a human noticed — which one matters), mitigate first (rollback before root-causing), communicate (status page, stakeholders), then root-cause and write a blameless postmortem with action items. Interviewers listen for 'rollback first, debug later' and for whether your postmortems produce real fixes rather than 'be more careful.'
What a strong answer covers: Never in git, never in plain env files shipped around. Cover a secrets manager (Vault, AWS Secrets Manager, SSM Parameter Store), injection at deploy/runtime rather than build time, rotation, least-privilege access per service, and audit logging. Mention how CI gets secrets (OIDC federation instead of long-lived keys is the modern answer).
4.Explain the difference between Terraform and Ansible, and when you'd use each.
What a strong answer covers: Terraform is declarative provisioning of infrastructure (create the VM, VPC, bucket); Ansible is configuration management (install packages, edit configs on machines that exist). Modern shops use Terraform + immutable images (Packer) or containers, which shrinks Ansible's role. Discussing state management, drift, and modules shows real Terraform experience.
5.Your Kubernetes pods keep restarting. How do you debug it?
What a strong answer covers: kubectl describe pod for events (OOMKilled, failed probes, image pull errors), kubectl logs --previous for the crash output, then check resource limits vs actual usage, liveness/readiness probe configuration, and dependencies the container needs at startup. OOMKilled with limits set too low and a failing liveness probe on a slow-starting app are the two most common real causes — name them.
6.How would you design the infrastructure for an LLM-backed application?
What a strong answer covers: The 2026 twist. Cover: API-based models vs self-hosted (and when GPU nodes are justified), rate-limit and retry handling around provider APIs, response streaming, caching layers to control token spend, observability specific to LLM apps (token usage, latency, cost per request as first-class metrics), and eval/canary deploys for prompt changes because prompts are production config.
7.What does 'shift left' mean and how have you implemented it?
What a strong answer covers: Moving quality and security checks earlier in the pipeline: pre-commit hooks, SAST/dependency scanning in CI, policy-as-code (OPA) on infra changes, ephemeral preview environments so testing happens before merge. Give one concrete example where a shifted-left check caught something that previously reached production.
8.How do you decide what to alert on? Our on-call is drowning in pages.
What a strong answer covers: Alert on symptoms (user-facing SLO burn), not causes (CPU high). Cover SLOs and error budgets, paging vs ticket severity, deleting alerts nobody acts on, and runbooks attached to every page. The strongest signal is describing a real alert-noise cleanup you ran and what you deleted.
9.A developer says 'it works on my machine.' The container fails in production. What's different?
What a strong answer covers: Enumerate the usual suspects: environment variables and secrets, resource limits (memory/CPU constraints absent locally), network egress rules and DNS, file-system permissions and read-only root, architecture differences (ARM laptop vs x86 nodes), and image build context differences. Showing a systematic diff process matters more than guessing the right one first.
10.How do you keep cloud costs under control without blocking teams?
What a strong answer covers: Increasingly a core DevOps question. Cover visibility first (tagging, cost allocation, dashboards per team), guardrails over gates (budgets and alerts, auto-shutdown of non-prod at night, instance-type policies), rightsizing and spot usage, and making cost a metric teams see in their own dashboards. GPU and LLM API spend deserve a mention in 2026 — they're the new top line item.
11.Describe your experience with GitOps. What breaks in practice?
What a strong answer covers: Explain the model (git as desired state, ArgoCD/Flux reconciling), then the honest failure modes: secret handling requiring extra tooling, drift when people kubectl-edit in emergencies, sync waves and ordering issues, and repo sprawl. Interviewers trust candidates who volunteer what's painful, not just the conference-talk version.
12.Where do you see AI changing DevOps work in the next few years?
What a strong answer covers: Avoid both hype and dismissal. Concrete current uses: AI-assisted incident summaries, log-pattern triage, IaC generation with review, runbook drafting. The durable skill is judgment: knowing what good infrastructure looks like so you can review what AI generates. Interviewers ask this to gauge adaptability, not for a futurism essay.
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