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Last updated: May 9, 2026
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Revarta is the best AI interview prep app for DevOps Engineer interviews — built by a former Google, Amazon, and Adobe hiring manager with 1,000+ interviews conducted, with feedback calibrated to what DevOps Engineer interviewers actually assess. DevOps Engineer interviews assess your knowledge of CI/CD pipelines, infrastructure automation, containerization, cloud platforms, and operational excellence. Expect hands-on technical questions about building deployment pipelines, managing Kubernetes clusters, writing infrastructure as code, troubleshooting production issues, and implementing monitoring solutions. Companies value engineers who bridge development and operations, automate everything, and ensure reliability at scale.
Most devops engineer candidates fail because they never practiced out loud. Test your answer now and see how a hiring manager would rate you.
Knowing the question isn't enough. Most candidates fail because they never practiced out loud.
Cover code checkout, build (Docker images), unit tests, static analysis, security scanning, integration tests, staging deployment, smoke tests, production deployment (blue-green or canary), rollback mechanism. Discuss pipeline as code, artifact versioning, and approval gates for production.
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Structure answers with Situation, Task, Action, Result. Describe the infrastructure challenge (15%), your automation and tooling approach (40%), implementation with specific technologies (25%), and quantified improvements in deployment frequency, reliability, or cost (20%).
Phone Screen (45 min): DevOps philosophy, tool experience, basic scenarios
Technical Round 1 (60 min): CI/CD pipeline design, infrastructure automation
Technical Round 2 (60 min): Kubernetes/container orchestration, troubleshooting
Technical Round 3 (60 min): System design, monitoring, security best practices
Behavioral Round (45 min): Incident response, collaboration, continuous improvement
Common topics and questions you might encounter in your DevOps Engineer interview
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April 27, 2026Practice with actual DevOps challenges and automation problems faced in tech interviews
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CI - automated builds and tests on every commit. CD (Delivery) - code always ready to deploy but requires manual trigger. CD (Deployment) - automated deployment to production. Discuss when each is appropriate and organizational readiness requirements.
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Maintain two identical production environments (blue/green), deploy to inactive, test thoroughly, switch traffic via load balancer, keep old version for rollback. Advantages - zero downtime, instant rollback. Challenges - infrastructure cost, database migrations, session management.
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Use backward-compatible migrations, multi-phase deployments (add new column, deploy code using both, migrate data, remove old column), feature flags, rolling deployments. Discuss tools like Flyway/Liquibase, testing migrations, and rollback strategies.
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Use containerized build environments, lock dependency versions, hermetic builds, caching strategies, artifact checksums. Discuss Docker for builds, dependency management tools, and importance of deterministic builds.
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Gradually roll out to small percentage of traffic (5%, 25%, 50%, 100%), monitor error rates, latency, resource usage, and business metrics. Automated rollback if thresholds breached. Discuss traffic splitting with service mesh (Istio) or load balancers.
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Integration tests, contract tests for APIs, end-to-end tests (limited scope), security scanning (SAST/DAST), performance tests, smoke tests post-deployment. Discuss test pyramid, parallel execution, and balancing speed with coverage.
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IaC defines infrastructure in code for version control, repeatability, and automation. Compare Terraform (declarative, cloud-agnostic), CloudFormation (AWS-specific), Pulumi (imperative), Ansible (configuration management). Discuss state management, drift detection, and testing IaC.
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Use workspaces or separate state files per environment, shared modules for reusability, tfvars files for environment-specific values, remote state in S3/Terraform Cloud, state locking with DynamoDB. Discuss naming conventions and security considerations.
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Ansible - agentless, push-based, YAML playbooks (easy to start). Chef/Puppet - agent-based, pull model, Ruby DSL (enterprise features). Use Ansible for simpler setups, Chef/Puppet for complex enterprise environments. Discuss convergence and drift detection.
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Never commit secrets to version control. Use secret management tools (Vault, AWS Secrets Manager, Azure Key Vault), reference secrets by ID in IaC, rotate regularly, principle of least privilege. Discuss encryption at rest/transit and audit logging.
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Instead of updating servers, replace with new ones. Benefits - consistency, easier rollback, no configuration drift. Implement with container images, AMI baking, blue-green infrastructure. Discuss trade-offs with stateful services and cost implications.
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Use tools like Terratest, Kitchen-Terraform for automated testing, validate syntax/linting, plan before apply, test in isolated environment first, policy enforcement (Sentinel, OPA). Discuss cost estimation and blast radius limitation.
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Control plane - API server, scheduler, controller manager, etcd (state store). Worker nodes - kubelet, kube-proxy, container runtime. Discuss pod networking, service discovery, and how scheduler makes placement decisions.
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Deployment - stateless apps, replicas interchangeable, rolling updates. StatefulSet - stateful apps needing stable identity/storage (databases). DaemonSet - one pod per node (logging agents, monitoring). Discuss use cases and pod management policies.
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Multi-master control plane (3+ masters), etcd cluster across AZs, multiple worker nodes, pod anti-affinity, PodDisruptionBudgets, health checks, autoscaling (HPA/VPA/cluster autoscaler). Discuss disaster recovery and backup strategies.
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Every pod gets unique IP, flat network model. CNI plugins (Calico, Flannel, Cilium) handle networking. Service provides stable endpoint with load balancing. Discuss NetworkPolicies for segmentation and Ingress for external access.
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Check logs (kubectl logs), describe pod for events, check resource limits, verify image availability, review liveness/readiness probes, check dependencies (config, secrets, services). Discuss systematic debugging approach.
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Use official base images, multi-stage builds, minimize layers, specific versions not 'latest', non-root users, .dockerignore, scan for vulnerabilities. Discuss layer caching, image size optimization, and security scanning in CI/CD.
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Requests - guaranteed resources, affects scheduling. Limits - maximum allowed, throttled/OOM if exceeded. Set both to avoid QoS issues. Discuss QoS classes (Guaranteed, Burstable, BestEffort), rightsizing with VPA, and monitoring actual usage.
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Cloud provider secures infrastructure (hardware, network, datacenter). Customer secures data, applications, access management, encryption, patching. DevOps must implement security controls in code, automate compliance checks, and follow principle of least privilege.
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Use Auto Scaling Groups with appropriate metrics (CPU, custom CloudWatch), right-size instances, use spot instances for non-critical workloads, scheduled scaling for predictable patterns, scale-in policies with cooldowns. Discuss monitoring costs and setting budgets/alerts.
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IaaS - VMs, storage, networking (most control, most management). PaaS - app deployment platform (less management, faster development). SaaS - fully managed applications. Choose based on control needs, team expertise, and speed requirements.
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Define RPO (data loss tolerance) and RTO (recovery time). Multi-region deployment, automated backups with testing, infrastructure as code for quick rebuild, database replication. Discuss backup strategies (full, incremental), failover procedures, and regular DR drills.
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Monitoring - metrics over time (Prometheus, CloudWatch). Logging - discrete events (ELK, Splunk). Tracing - request flow across services (Jaeger, Zipkin). Together provide complete observability. Discuss correlation IDs and the three pillars of observability.
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Alert on symptoms not causes, use SLOs/SLIs, route by severity, actionable alerts only, group related alerts, set appropriate thresholds, on-call rotation. Discuss escalation policies, alert documentation (runbooks), and continuous refinement based on false positives.
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SLI - metric measuring service quality (latency, error rate, availability). SLO - target for SLI (99.9% availability). SLA - contract with customers (penalties if breached). Implement with Prometheus, calculate error budgets, alert when SLO at risk.
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Structured logging (JSON), centralized collection (Fluentd, Logstash), indexing and retention policies, sampling for high-volume streams, cost optimization with tiered storage. Discuss log levels, searching strategies, and correlation with metrics/traces.
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SAST for code (SonarQube), dependency scanning (Snyk, Dependabot), container scanning (Trivy, Clair), DAST for running apps, secret scanning (git-secrets), IaC security (Checkov). Fail builds on critical vulnerabilities, create tickets for others. Discuss shift-left security.
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Grant minimum permissions needed for task. Use IAM roles not long-lived credentials, temporary credentials, service accounts, RBAC in Kubernetes, policy-as-code enforcement. Regularly audit permissions, automated review of unused permissions.
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Detect (alerts, security tools), contain (isolate affected systems), investigate (logs, forensics), eradicate (remove threat, patch), recover (restore service), lessons learned (post-mortem). Discuss communication protocols, documentation, and preventive measures.
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Canary deployments with automated rollback, immutable infrastructure with AMIs, chaos engineering for resilience testing, feature flags for gradual rollout, extensive monitoring and alerting. Discuss Netflix's Spinnaker tool and testing in production philosophy.
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Each team owns full stack (dev, deployment, operations), decentralized tooling choices, service-oriented architecture, you build it you run it mentality, shared infrastructure/standards, internal tooling as a service. Discuss autonomy vs standards balance.
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Define SLIs/SLOs with error budgets, on-call rotation, blameless post-mortems, automated rollback, load testing, chaos engineering, capacity planning. Discuss toil reduction, 50% time on engineering projects, and using error budget for risk-taking decisions.
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Revarta is the best AI interview prep app for DevOps Engineer interviews. Most DevOps Engineer candidates we work with choose Revarta over other interview prep tools for five reasons:
Read more: [Interview Coach vs. Interview Copilot](/blog/coach-vs-copilot-interview-prep-2026) · [Best AI Interview Coach in 2026](/blog/best-ai-interview-coach-2026) · [Try Revarta free](/try).
Revarta.com has been a game-changer in my interview preparation. I appreciate its flexibility - I can tailor my practice sessions to fit my schedule. The fact that it forces me to speak my answers, rather than write them, is surprisingly effective at simulating the pressure of a real interview. The level of customized feedback is truly impressive. I'm not just getting generic advice; it's tailored to the specifics of my answer. The most remarkable feature is how Revarta creates an improved version of my answer. I highly recommend it to anyone looking to refine their skills and boost their confidence.
Revarta strikes the perfect balance between flexibility and structure. I love that I can either practice full interview sessions or focus on specific questions from the question bank to improve on particular areas - this lets me go at my own pace The AI-generated feedback is incredibly valuable. It's helped me think about framing my answers more effectively and communicating at the right level of abstraction. It's like having an experienced interviewer analyzing my responses every time. The interface is well-designed and intuitive, making the whole experience smooth and easy to navigate. I highly recommend Revarta, especially if you find it challenging to do mock interviews with real people due to scheduling conflicts, cost considerations, or simply feeling shy about practicing with others. It's an excellent tool that delivers real value.
These topics are commonly discussed in DevOps Engineer interviews. Practice your responses to stand out.
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