Interview Preparation
DevOps Interview Questions and Answers for Experienced Professionals — 2026
These are the most commonly asked DevOps interview questions for experienced professionals in 2026 — compiled by Vtricks faculty based on real interview feedback from students placed at companies like IBM, Cisco, Amazon AWS, Microsoft, Infosys, Wipro in Bangalore.
There are currently 3,500+ active DevOps job openings in Bangalore. Freshers can expect ₹5–8 LPA at companies across Bangalore's tech corridor — Whitefield, Electronic City, Koramangala, and the CBD. Preparation matters: candidates who practise these questions consistently perform significantly better in technical rounds.
Interview Tips from Vtricks Faculty
- Always explain your reasoning process — interviewers want to see how you think, not just the final answer.
- Use real examples from projects you have worked on when answering scenario-based questions.
- If you don't know the answer, say so honestly and describe how you would find the answer — this is better than guessing.
- For Bangalore companies specifically: be ready to answer follow-up questions — they often go 2-3 levels deep on any concept.
- Always ask clarifying questions before answering complex scenario-based questions — this demonstrates professional problem-solving approach.
Easy — basic concept check
Medium — applied knowledge
All 15 Questions
DevOps Interview Questions — Experienced Professionals
Q1. What is GitOps and how does it differ from traditional CI/CD?
Conceptual
Hard
ANSWER
GitOps is an operational framework that uses Git as the single source of truth for both application code and infrastructure configuration. In GitOps: all desired system states are declared in Git, automated agents reconcile actual state with desired state continuously. Tools: ArgoCD and Flux are the main GitOps operators for Kubernetes. Traditional CI/CD: pipeline is triggered by code push, pipeline deploys to environment. GitOps: you commit changes to Git, the GitOps operator detects drift between Git and the cluster and applies changes automatically. Benefits: full audit trail in Git history, easy rollbacks (revert a commit), consistent environments, pull-based deployment eliminates the need to give CI/CD pipelines direct access to production clusters.
Q2. How do you implement zero-downtime deployments in Kubernetes?
Technical
Hard
ANSWER
Strategies for zero-downtime deployments in Kubernetes: Rolling Update (default) — gradually replaces old pods with new ones, controlled by maxSurge (extra pods during update) and maxUnavailable (pods that can be down during update). Blue-Green — run two identical Deployments, switch Service selector to point to new version. Canary — deploy new version to a small percentage using multiple Deployments with different replica counts or service mesh traffic splitting. Pod Disruption Budget — sets minimum available pods during voluntary disruptions. Readiness Probes — Kubernetes only routes traffic to pods that pass readiness checks, preventing traffic to pods that are not ready to serve.
Q3. Explain Kubernetes networking — Services, Ingress, and Network Policies.
Technical
Hard
ANSWER
Services provide stable network endpoints for pods: ClusterIP (internal only), NodePort (expose on each node's IP), LoadBalancer (creates cloud load balancer), ExternalName (maps to external DNS). Ingress is an API object that manages external HTTP/HTTPS access to Services within a cluster — it provides URL routing, SSL termination, and load balancing at the HTTP level using an Ingress Controller (nginx-ingress, traefik, AWS ALB). Network Policies control which pods can communicate with each other and with external endpoints — they act as a firewall at the pod level, enforced by the CNI plugin (Calico, Cilium). Without Network Policies, all pods can communicate with all other pods by default.
Q4. What is Helm and how does it help with Kubernetes deployments?
Technical
Medium
ANSWER
Helm is the package manager for Kubernetes — it packages, versions, and deploys Kubernetes applications using Charts (collections of YAML templates). Benefits: templating allows the same Chart to be used across dev, staging, and production with different values; versioning allows rollbacks with helm rollback; Chart repositories for sharing reusable deployments; manages complex multi-component applications as a single release. Helm commands: helm install, helm upgrade, helm rollback, helm uninstall, helm repo add. Helm 3 removed Tiller (a security concern in Helm 2) and communicates directly with the Kubernetes API server using kubeconfig credentials.
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Q5. How do you secure a Kubernetes cluster?
Technical
Hard
ANSWER
Kubernetes security layers: API Server — enable RBAC (Role-Based Access Control), disable anonymous access, use TLS for all communication, audit logging. Node security — regular OS patches, CIS benchmark hardening, disable unnecessary services. Pod security — use non-root users, read-only root filesystem, drop capabilities, use SecurityContext. Network — implement Network Policies to restrict pod-to-pod communication, use service meshes (Istio) for mTLS. Secrets — use external secret managers (AWS Secrets Manager, HashiCorp Vault) rather than Kubernetes Secrets (stored as base64 in etcd). Image security — scan images with Trivy or Clair, use signed images, pull from private registries only.
Q6. Explain Terraform state management and remote backends.
Technical
Hard
ANSWER
Terraform state is a JSON file that maps Terraform resources to real infrastructure — it is the source of truth for what Terraform manages. Local state (default) is stored in terraform.tfstate on disk — not suitable for teams. Remote backends store state remotely: AWS S3 + DynamoDB for locking (most common), Terraform Cloud, Azure Blob Storage, Google Cloud Storage. State locking prevents concurrent runs from corrupting state. Key commands: terraform state list (view managed resources), terraform state mv (move resources), terraform import (import existing infrastructure). Never manually edit state files. Use workspaces for managing multiple environments with the same configuration.
Q7. What is a service mesh and when do you need one?
Conceptual
Hard
ANSWER
A service mesh is a dedicated infrastructure layer for handling service-to-service communication in a microservices architecture. It provides: mutual TLS (mTLS) for encryption and authentication between services, advanced traffic management (circuit breaking, retries, timeouts, canary releases), observability (distributed tracing, metrics, access logs) without code changes. Istio is the most popular service mesh; Linkerd is a simpler alternative. You need a service mesh when: you have many microservices with complex communication patterns, you need fine-grained traffic control, you require zero-trust networking. Service meshes add complexity — do not adopt until you have real problems they solve.
Q8. How do you handle secrets management in a DevOps pipeline?
Technical
Hard
ANSWER
Never store secrets in code or Git. Proper secrets management: use dedicated secret managers — AWS Secrets Manager or Parameter Store, HashiCorp Vault, Azure Key Vault. In CI/CD pipelines: store secrets as encrypted environment variables in Jenkins credentials, GitHub Actions secrets, or GitLab CI variables — they are injected at runtime. In Kubernetes: use external-secrets-operator to sync secrets from AWS Secrets Manager to Kubernetes Secrets. Vault Agent Injector sidecar injects secrets into pods. Rotate secrets regularly. Audit secret access. Use short-lived credentials and IRSA (IAM Roles for Service Accounts) in AWS to avoid static credentials.
Q9. What is observability in distributed systems and how do you implement it?
Technical
Hard
ANSWER
Observability in distributed systems means being able to understand what any service is doing at any time from external outputs. Three pillars: Metrics — quantitative measurements over time (request rate, error rate, latency) collected with Prometheus, visualised in Grafana. Create SLOs and alert when SLOs are breached. Logs — structured logs (JSON) from all services, centralised with ELK stack or Loki + Grafana. Traces — distributed traces showing request flow across multiple services, collected with OpenTelemetry, visualised in Jaeger or Tempo. Use RED method for services (Rate, Errors, Duration) and USE method for infrastructure (Utilisation, Saturation, Errors).
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Q10. Explain the concept of chaos engineering.
Conceptual
Hard
ANSWER
Chaos engineering is the practice of intentionally injecting failures into production systems to proactively discover weaknesses before they cause real outages. Originated at Netflix with Chaos Monkey (randomly terminates production instances). Process: define steady state (normal system behaviour with metrics). Hypothesise that system maintains steady state during failure. Inject failure (kill instances, slow network, exhaust CPU, corrupt data). Observe if hypothesis holds. Fix weaknesses discovered. Tools: Chaos Monkey, Gremlin, Litmus (Kubernetes-native), AWS Fault Injection Simulator. Start with small blast radius, run in controlled environments first. Document findings and track improvements.
Q11. How do you design a highly available infrastructure on AWS?
Technical
Hard
ANSWER
HA design principles: Multi-AZ deployment — spread resources across multiple Availability Zones (at minimum 2, ideally 3). Auto Scaling Groups — automatically replace failed instances and scale based on load. RDS Multi-AZ — synchronous replication to standby instance, automatic failover. ElastiCache in Multi-AZ mode. Application Load Balancer — routes traffic across AZs, health checks remove unhealthy instances. Route53 health checks with DNS failover for multi-region. S3 for static assets — 11 nines durability across multiple AZs. CloudFront CDN for global content delivery. Define RTO (Recovery Time Objective) and RPO (Recovery Point Objective) to drive architecture decisions. Target 99.99% availability for critical services.
Q12. What is the difference between horizontal and vertical scaling?
Conceptual
Medium
ANSWER
Vertical scaling (scale up) — increasing resources on a single server (add more CPU, RAM, storage). Simple to implement, no code changes needed, but has a ceiling (maximum hardware size), creates a single point of failure, requires downtime for some changes, expensive at high levels. Horizontal scaling (scale out) — adding more servers/instances to distribute load. More complex (requires stateless applications, load balancing, distributed systems), but theoretically limitless, eliminates single points of failure, more cost-effective at scale. Modern cloud architectures prefer horizontal scaling with auto-scaling groups, containers, and stateless application design.
Q13. Explain the 12-factor app methodology.
Conceptual
Hard
ANSWER
The 12-factor app is a methodology for building modern, cloud-native applications: 1) Codebase — one codebase in version control. 2) Dependencies — explicitly declare and isolate. 3) Config — store in environment (not code). 4) Backing services — treat as attached resources. 5) Build/Release/Run — strict separation. 6) Processes — stateless, share nothing. 7) Port binding — export services via port. 8) Concurrency — scale via process model. 9) Disposability — fast startup and graceful shutdown. 10) Dev/Prod parity — keep environments similar. 11) Logs — treat as event streams. 12) Admin processes — run as one-off processes. Following these principles makes applications portable, scalable, and DevOps-friendly.
Q14. How do you manage multi-environment infrastructure with Terraform?
Technical
Hard
ANSWER
Strategies for multi-environment Terraform: Terraform Workspaces — separate state per workspace (dev, staging, prod). Simple but can lead to workspace sprawl and does not handle major environment differences well. Directory structure — separate directories per environment (environments/dev, environments/staging, environments/prod) each with their own terraform.tfvars. Allows maximum customisation per environment. Modules — reusable modules parameterised with different values per environment. Terragrunt — a wrapper for Terraform that reduces code duplication with DRY configurations. Best practice: treat production infrastructure differently — separate AWS accounts, stricter access controls, approval workflows before terraform apply.
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Q15. What is eBPF and how is it changing observability in Kubernetes?
Technical
Hard
ANSWER
eBPF (extended Berkeley Packet Filter) is a Linux kernel technology that allows running sandboxed programs in the kernel without changing kernel source or loading kernel modules. It is changing observability because it enables zero-instrumentation observability — you can trace system calls, network traffic, and application behaviour without modifying applications or injecting sidecars. Tools using eBPF: Cilium (networking and security), Tetragon (security observability), Parca (continuous profiling), Pixie (auto-telemetry for Kubernetes). Benefits over traditional methods: lower overhead than sidecar proxies (like Istio), works with any programming language without agent installation, kernel-level visibility impossible with application-level instrumentation.
Company Insights
What DevOps Companies in Bangalore Actually Ask
Based on interview feedback from Vtricks students placed at Bangalore companies in 2026:
Round 1 — Written/Online Test
Most Bangalore companies start with a written or online test covering devops fundamentals, multiple choice questions on Docker and Kubernetes, and basic problem-solving questions. Duration: 30–60 minutes. Companies like IBM and Cisco use platforms like HackerRank or their own internal assessments.
Round 2 — Technical Interview (Most Important)
This is where most candidates are filtered. Expect: direct questions from this list, hands-on tasks (write a SQL query, debug a piece of code, explain a dashboard you built), and scenario-based questions where you walk through how you would solve a real problem. Be prepared to share your screen and code live.
Round 3 — Managerial / HR Round
Focuses on: why you chose devops as a career, how you handle ambiguous requirements, a project you are proud of (have this ready in detail — situation, what you did, result), and salary expectations. Research the company's tech stack and recent news before this round.
Tools You Must Be Able to Demonstrate
- Docker — be ready to use this live in an interview
- Kubernetes — be ready to use this live in an interview
- Jenkins — be ready to use this live in an interview
- Terraform — be ready to use this live in an interview
- AWS — be ready to use this live in an interview
- Azure — be ready to use this live in an interview
More Resources
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