DevOps Interview

Prepare for DevOps interviews with practical knowledge, structured answers, and real-world engineering scenarios.

How would you investigate a Kubernetes workload that keeps restarting? Recover from a partially completed Terraform deployment? Design a secure CI/CD pipeline-or explain why a healthy AI endpoint can still produce poor results?

DevOps Interview helps you work through these questions and communicate your reasoning clearly. Across 22 chapters, this comprehensive guide combines technical explanations, interview questions and answers, troubleshooting exercises, coding practice, and system design scenarios.

Build your understanding from Linux, networking, Git, and automation through cloud infrastructure, containers, Kubernetes, production operations, and the delivery of data and AI systems.

Inside, you will explore:

• DevOps foundations and interview techniques: Collaboration, shared ownership, engineering roles, and methods for explaining experience through outcomes and tradeoffs.

• Linux, networking, and automation: Processes, permissions, resource troubleshooting, DNS, HTTP, TLS, Git workflows, Bash scripting, and Python automation.

• Cloud infrastructure and configuration: Availability zones, networking, identity, storage, autoscaling, Terraform state, drift, partial failures, and Ansible.

• Docker and Kubernetes: Image builds, container isolation, persistent storage, workload controllers, scheduling, probes, RBAC, Helm, scaling, and operational troubleshooting.

• CI/CD and release engineering: GitHub Actions, Jenkins, reproducible builds, immutable artifacts, approval controls, GitOps, Argo CD, Flux, and deployment strategies.

• Security and production reliability: Secrets, software supply chain security, policy as code, OpenTelemetry, Prometheus, Grafana, SLOs, incident response, and disaster recovery.

• Performance and cloud costs: Load testing, capacity planning, bottleneck analysis, rightsizing, Spot capacity, and cost per unit of useful work.

• DataOps, MLOps, and generative AI: Reliable data pipelines, model evaluation and promotion, GPU serving, drift monitoring, RAG evaluation, prompt injection, and agent tool permissions.

Practice explaining decisions under realistic conditions.

Work through seven system design scenarios covering microservices delivery, shared Kubernetes platforms, infrastructure across cloud accounts, batch and streaming pipelines, ML platforms, production RAG applications, and regional disaster recovery.

Strengthen your preparation with Bash and Python exercises, Terraform and Kubernetes configuration reviews, incident investigations, behavioral questions, and mock interview sets by engineering role. Quick-reference appendices help you revisit essential commands and delivery patterns.

Who this book is for

DevOps engineers, platform engineers, site reliability engineers, cloud engineers, and software, data, and AI/ML engineers preparing for interviews or expanding their production engineering skills.

Develop the technical understanding and communication skills to explain what you would do, why you would do it, and how you would verify the result.