Preparing for modern DevOps interviews in 2026

A September 2026 preparation plan should cover the systems you will operate and the changes that affect their safety. Ask for the interview outline, read the role responsibilities and identify the team's stack before choosing specialist topics. There is no single DevOps loop shared by every employer.

Start with systems you can explain

Be able to trace a request through DNS, a load balancer and an application to its dependencies. Explain how CPU time, memory, queues and connection pools limit that path. Practise distinguishing a symptom from its cause: exit code 137 indicates a SIGKILL outcome, while an OOM diagnosis needs supporting evidence.

For Kubernetes roles, follow a workload from admission through scheduling, image pull, startup and readiness. For infrastructure as code, explain state ownership, locking, drift and recovery from a partial apply. These mechanisms stay useful when tools and versions change.

Add changes with operational consequences

Ingress migration. The Kubernetes project retired the community ingress-nginx controller in March 2026. Existing installations can continue to run, but no further releases or security fixes are provided. Prepare to inventory controller-specific behavior and plan a migration. Converting an Ingress object to an HTTPRoute is only part of the work; authentication, timeouts and connection draining need tests too. See the Kubernetes retirement statement.

Delivery measurement. DORA now describes five software delivery metrics, including deployment rework rate. Practise calculating them from deployment records and stating the denominator. A deployment failure and an unrelated infrastructure outage do not belong in the same recovery metric. The DORA guide defines the current measures; our worked concept applies them to an example.

Platform decisions. Prepare an example where a shared platform removes repeated work, then explain how you would measure adoption, support load and the time needed to complete a developer task. Include an escape path for unsupported workloads. A portal with many templates is not evidence that teams can ship safely.

Credentials and automation. Trace which identity can obtain a credential, what it can access and how it expires. Apply the same questions to an AI agent reading a pull request: the input can be hostile even when the process holding the credential is trusted. Tool authorization and limits on external effects need enforcement outside the model's prompt.

Cost per useful outcome. Compare spend with a denominator such as completed jobs or successful transactions, and keep reliability and quality visible. A cheaper request that fails or needs manual repair can increase the cost of delivering the result. The FinOps Framework provides a broader vocabulary for allocation, forecasting and business value.

Choose AI infrastructure depth deliberately

If the role operates models, learn time to first token, inter-token latency, batching, cache memory and model loading. Requests with different token counts and context lengths can consume very different amounts of work. Autoscaling still has familiar concerns such as queues, startup time and draining, but CPU utilisation alone may miss the limiting resource.

GPU scheduling also needs a precise account of the platform. Device allocation, sharing and isolation depend on hardware and configuration. A free GPU does not guarantee a pending workload can use it: affinity, topology, quota or another resource may prevent placement.

Model rollout needs quality checks alongside availability and latency. Ordinary software can also return a successful status with an incorrect result, so this extends correctness testing rather than replacing it. Use representative evaluation cases and state which failures the checks cannot detect.

For roles that use a hosted model API, spend more time on provider outages, quotas, data handling, authorization and cost controls than on training collectives or GPU drivers.

What our source comparison can tell you

Our repository review counted 657 Linux and networking prompts and two AI infrastructure prompts across five selected public question banks. Of those, 533 Linux prompts and zero AI prompts were corroborated across repositories under our matching method. This measures the selected repositories, not interview frequency or all available preparation material.

The homepage compares those corroborated counts against editorial topic targets for a 300-question curriculum. The targets are our choices. A high ratio does not mean Linux is unimportant, and a low ratio does not prove a topic appears in your interview.

A practical preparation session

Pick a failure in a disposable lab, write down the impact and narrate the next observation you need. Check your diagnosis against the evidence, choose a safe mitigation and explain what could make it fail. Then solve one design problem with explicit capacity numbers and rehearse one real experience with accurate attribution of your contribution.

Use the troubleshooting bank for the diagnostic exercise and the AI infrastructure questions when they match your target role. Return to the job description after each session: the useful question is which responsibility you can now explain or demonstrate.