DevOps remains one of the most sought-after skill sets today. With companies shifting rapidly toward cloud infrastructure, CI/CD automation, containerisation, and platform engineering, demand for strong DevOps engineers is at an all-time high — and so is the competition.
According to the 2024 Puppet State of DevOps Report, 58% of organisations say Platform Engineering increases productivity, and 50% report faster product delivery — two of the core outcomes strong DevOps teams are expected to drive.
This guide breaks down the most common DevOps interview questions, real-world scenarios, and practical examples you'll be expected to navigate, from Kubernetes troubleshooting and Terraform fundamentals to CI/CD pipelines and cloud architecture. But DevOps interviews aren’t only about technical depth. Cultural fit matters just as much, and interviewers want to understand how you collaborate, communicate, handle pressure, and contribute to a healthy engineering culture.
Later in this guide, you’ll find a full section on behavioural questions and example answers to help you prepare for that part of the process too.
Core DevOps concepts and interview strategies
CI/CD, GitOps and automation fundamentals
Cloud platforms: AWS, Azure, GCP (with real scenarios)
Containers, Kubernetes, Helm, and service orchestration
Infrastructure as Code (Terraform, CloudFormation)
Observability, monitoring and SRE-style questions
Real-world troubleshooting and incident questions
Senior-level architecture and system design
Sample coding/scripting challenges (Bash, Python)
Use this as your go-to resource for any DevOps or platform engineering interview.
For broader preparation, explore our comprehensive Technical Assessment Preparation Guide.
Most DevOps interviews start with fundamentals: reliability, automation, collaboration, and continuous delivery. Expect interviewers to test not just your tooling knowledge, but your mental model of how modern engineering teams ship software.
"DevOps is about shortening the path from idea to production by improving collaboration between dev and ops, automating repeatable steps, and building systems that are reliable and observable. In my last role, we reduced deployment time from hours to minutes by introducing CI/CD, automated testing, and better alerting."
CI vs CD vs continuous deployment
Immutable infrastructure
Blue-green vs rolling deployments
Secrets management
Shift-left testing and security
Tip: Keep a few real examples ready: a deployment pipeline you built, a flaky service you stabilised, or a monitoring setup you improved.
CI/CD is at the heart of modern DevOps practice. Interviewers want to know whether you can design pipelines that are reliable, secure, and fast enough to support real engineering teams.
For additional context on how CI/CD ties into API lifecycles and backend workflows, the Backend Engineer Interview Guide offers helpful examples you can build on.
Triggers: PR events, branch protection rules, semantic versioning
Build stage: dependency caching, parallel builds, artefact packaging
Automated testing: unit, integration, contract tests, API tests
Security: SAST, SCA, container scanning, signing images
Deployment: blue/green, rolling, canary releases; environment-specific configs
Post‑deploy checks: health checks, smoke tests, automated rollbacks
Observability hooks: logs, metrics, and tracing from deployment events
Practical example you can reuse:
"At my last job, we re-architected our CI/CD so that services built in parallel, ran fast unit tests first, and only triggered integration tests for changed modules. Deployments used a progressive rollout where 5% of traffic hit the new version before full rollout. This reduced incidents and cut total pipeline time from 18 minutes to 6."
GitHub Actions, GitLab CI, Jenkins, CircleCI
ArgoCD for GitOps-style deployments
ECR/ACR/GCR for image storage
OPA or Conftest for pipeline governance
Mini challenge: Write a workflow that builds a Docker image, runs tests, signs the image, pushes it to ECR, and triggers a canary deployment on Kubernetes.: Write a GitHub Actions workflow that builds a Docker image, runs tests, and pushes it to ECR.
Most DevOps teams rely heavily on Kubernetes, so expect deep questions on cluster design, debugging, deployments, and optimisation.
Run kubectl describe pod to inspect events
Check liveness/readiness probe failures
Inspect logs using kubectl logs
Verify env vars, config maps, and secret mounts
Inspect resource limits—OOMKilled is common
Inspect container startup commands and entrypoints
Check init containers
Check image pull issues
Verify permissions/service accounts
Look at node-level issues
Cluster architecture: control plane vs nodes
Deployments vs StatefulSets vs DaemonSets
Horizontal/vertical autoscalers
Network Policies and service-to-service communication
Pod disruption budgets
Namespace strategies and multi-tenancy
Helm chart design and templating
If you want to strengthen your understanding of distributed systems and how Kubernetes concepts map to high-level architecture discussions, explore our System Design Interview Preparation Guide.
"We reduced cluster costs by 32% by right-sizing memory limits, switching large workloads to spot nodes, and using pod autoscaling more intelligently."
Pro tip: Interviewers love real-world K8s migration or optimisation stories—bring one.: If you've migrated services to K8s or reduced cluster costs with better autoscaling, talk about it.
Most DevOps interviews assume at least one cloud provider. What they want to see is whether you understand how services work together, and when to choose one architecture over another.
Networking: VPC, subnets, route tables, NAT gateways, security groups
Compute: EC2 vs Fargate vs Lambda; when to pick each
Storage: RDS, DynamoDB, S3 lifecycle rules, backups
Security: IAM roles, least privilege, KMS
Reliability: Multi-AZ setups, autoscaling, load balancers
Monitoring: CloudWatch metrics, logs, alarms
Cost considerations (e.g., NAT costs, storage tiers)
Deployment strategies (e.g., blue/green with ALB)
Disaster recovery (RPO/RTO, cross-region replication)
Caching (CloudFront, ElastiCache)
"To keep costs predictable, we used DynamoDB on-demand for spiky workloads and added TTL-based expiration to reduce storage."
IaC is one of the strongest signals of DevOps maturity. Terraform remains the most commonly tested.
Sample question: How do you structure Terraform for a large system?
Break infrastructure into versioned modules
Use remote state (S3 + DynamoDB, Terraform Cloud)
Enforce format, validate, plan in CI
Use workspaces or separate directories per environment
Pin provider versions to avoid breaking updates
Use policy-as-code for governance
"We introduced a module registry that every team used, ensuring shared patterns for VPCs, IAM roles, and databases. This reduced security issues and drift across environments."
Detecting drift automatically
Using depends_on carefully to avoid unnecessary rebuilds
Handling secrets with SSM, Vault, or Secret Manager
Importing existing cloud resources safely)
This is often the make-or-break section for senior candidates.
Sample question: How do you design an alerting system that avoids noise?
Alert on symptoms, not raw metrics
Use SLO-based thresholds
Include actionable detail in alerts
Use structured logs and distributed tracing
Document runbooks for common issues
Regularly review and prune noisy alerts
Migrating from manual dashboards to SLO-based alerting
Reducing alert fatigue through silence windows and deduplication
Adding tracing that helped debug latency spikes
Running blameless post-incident reviews with clear follow-up actions
Prometheus metrics best practices (RED/USE patterns)
Grafana dashboards for performance analysis
OpenTelemetry for unified tracing
Error budget policies and how they influence deployment cadence
These evaluate how you think—your calmness, clarity, and structure.
Common scenarios:
"CPU on this node is 100%. What do you check first?" (Check per-pod usage, runaway processes, node logs)
"A deployment succeeded but the service is returning 500s." (Check readiness probes, logs, config changes)
"Your pipeline slowed down by 5× today." (Check worker queue congestion, external dependencies, caching)
"Traffic doubled overnight and pods won't scale." (Check HPA metrics, cluster autoscaler events, resource quotas)
"A pod works locally but fails in the cluster." (Check environment parity, DNS, networking policies)
Tip: Interviewers care more about your reasoning process than your final answer..
These tests are short, practical, and reflect real tasks you’ll automate on the job.
Find the top 10 largest files in a directory
Parse logs and extract error counts
Write a script that checks service health and restarts on failure
Automate S3 backups with versioning
Write a Python script that verifies IAM permissions
Build a CLI tool that validates Kubernetes YAML
Readability
Clear variable naming
Use of functions instead of duplicated logic
Error handling
Comments that explain intent.
| What they ask | What they’re really assessing |
|---|---|
| "Explain your CI/CD process" | Can you design reliable, secure, and scalable delivery pipelines? |
| "How would you deploy this service?" | Do you understand cloud architecture, tradeoffs, and reliability? |
| "Tell me about difficult incidents you've handled" | Can you debug calmly, communicate clearly, and follow structured reasoning? |
| "How do you optimise Kubernetes costs?" | Are you pragmatic about production, resource usage, and scaling? |
| "Here’s a YAML configuration issue — how would you approach debugging it?" | Can you troubleshoot quickly, safely, and methodically under pressure? |
| "What monitoring would you set up for a new service?" | Do you think like an SRE about reliability, SLOs, and observability? |
Linux basics
Git fundamentals
CI/CD basics
Containers (simple Dockerfiles)
Cloud fundamentals
Basic Terraform
Logging and metrics basics
Tip: Focus on understanding fundamentals deeply. Interviewers don’t expect mastery, but they want to see that you can learn fast, ask good questions, and automate small tasks confidently. Show curiosity and eagerness to automate.
Kubernetes fundamentals
CI/CD security and optimisation
Terraform modules and best practices
Cloud networking (ALB, NLB, routing)
Incident response stories
Prometheus/Grafana
Autoscaling strategies
Tip: Bring specific examples of problems you’ve solved—slow pipelines, failing deployments, scaling bottlenecks, broken infrastructure. Mid-level interviews reward real stories over theory.
Cloud architecture and multi-region design
Kubernetes internals
GitOps (ArgoCD, Flux)
SRE principles
Cost optimisation
Advanced Terraform
Complex incident leadership
Tip: Senior interviews focus on tradeoffs, communication, and system-wide thinking. Explain why you made architectural decisions, how you prevented issues, and how you improved reliability across teams.
Strong DevOps engineers aren’t evaluated only on technical skills, they’re assessed on how they communicate, collaborate, make decisions under pressure, and contribute to a healthy engineering culture.
DevOps is fundamentally about people, processes, and shared responsibility, so interviewers often ask behavioural questions to understand how you operate in real-world environments.
"Tell me about a time you fixed a broken process."
What they’re assessing: whether you take initiative, reduce friction, and improve workflows rather than accepting dysfunction.
Example answer:
"At my last company, deployments required manual approvals from three teams, which often delayed releases. I mapped out the workflow, identified what could be automated, and worked with engineering managers to implement automated checks and streamlined approvals. This reduced deploy time from hours to under 20 minutes and gave teams more confidence in shipping."
Example answer:
"We experienced a major outage caused by a misconfigured Kubernetes ingress. During the incident, I coordinated updates in Slack, rolled back the change, and added temporary rate limiting to stabilise traffic. Afterward, I led a blameless postmortem that resulted in better config validation and automated canary checks to prevent similar issues."
Example answer:
"A developer wanted to disable a failing test to speed up delivery. Instead of blocking the change outright, I asked about the impact of the test and we realised it had caught three production issues in the past quarter. We agreed to temporarily quarantine the flaky test while we fixed it. This kept reliability intact without slowing delivery."
Example answer:
"Our team manually rotated logs and archived them weekly. I automated the process using S3 lifecycle rules and a small Lambda function. This saved about 4 hours a week across the team and eliminated a recurring source of human error."
Example answer:
"We had a critical feature deadline, but our integration tests were unstable. Instead of skipping them entirely, I proposed running a reduced suite focused on the highest-risk paths and enabling canary deployment with automatic rollback. This allowed us to ship on time without compromising reliability."
Prepare 3–4 stories that demonstrate ownership, collaboration, and problem-solving.
Use a simple structure (Situation → Action → Result → Learning).
Show how you communicate during incidents, not just how you fix things.
Speak honestly about mistakes and what you learned; interviewers value growth mindset.
Highlight cross-team work.
DevOps interviews in 2025 aren't just about tools. They're about building reliable systems, thinking clearly under pressure, and automating everything that slows teams down.
Next steps:
Practise explaining architecture diagrams
Build a mini project with Terraform + K8s
Run mock interviews with another engineer
Keep track of weak spots and revisit them
You've got this.
What are the most commonly asked DevOps interview questions in 2025?
Expect topics such as CI/CD pipelines, Kubernetes troubleshooting, Terraform, cloud architecture, observability, and Linux fundamentals.
How should I prepare for a DevOps technical interview?
Build small end-to-end projects: IaC + Docker + Kubernetes + CI/CD. Practise troubleshooting simulations.
Do I need to know Kubernetes for DevOps roles?
Yes for most cloud-native companies. You should understand deployments, probes, logs, networking basics, and debugging.
What scripting skills are required?
Comfort with Bash is essential. Python is increasingly common for automation and cloud tooling.
What cloud concepts should I revise?
VPC design, IAM, load balancing, autoscaling, storage options, cost optimisation, and backup strategies.
How important is Terraform in DevOps interviews?
Very. It's the default IaC tool for many teams and appears frequently in mid and senior interviews.
What troubleshooting questions should I expect in a DevOps interview?
Expect issues with crashing pods, failing health checks, pipeline slowdowns, scaling issues, and permissions errors.
How do I stand out in a DevOps interview?
Use real examples: migrations you led, outages you stabilised, costs you reduced, or pipelines you improved.