DevOps is the most demanding field in the realm of IT services now-a-days. It is undoubtedly future-safe‒the technology may keep evolving and changing but the domain will remain there, with high demand of mid and senior level DevOps engineers. If you’re dreaming to adopt a life-long IT pro career and ride the wave of excellence, DevOps is one of the best to choose from.
The role of a DevOps Engineer continues to evolve rapidly in 2025 and the years to come, as organizations are in constant endeavor to achieve faster and more reliable software delivery pipelines.
If you’re preparing for a DevOps interview, especially at a fresh or mid level, you’re expected to be hands-on with automation, infrastructure as code, cloud platforms, CI/CD, containers, monitoring, and security.
This article covers the maximum of essential DevOps interview questions to help you get prepared for your next job interview.
1. What is DevOps and how does it differ from Agile?
DevOps is a cultural and technical movement aimed at constantly unifying software development (Dev) and IT operations (Ops) for faster, consistent, and more reliable software delivery.
While Agile, at its core, focuses on software development practices, DevOps includes Agile but also encompasses operational aspects, infrastructure automation, continuous integration, deployment, and monitoring, and these activities go on for the life-time of the business functions.
2. What is a DevOps Engineer?
A DevOps Engineer bridges the gap between software development and IT operations, leveraging expertise in both areas to optimize the software development lifecycle. Their mission is to deliver high-quality software rapidly, efficiently, and reliably. By automating and integrating processes, DevOps Engineer enables seamless collaboration between development and operations teams, driving continuous delivery and integration of software.
Nevertheless, it might become a thankless job for them if the standard DevOps methods, tools and practices are not utilized effectively across teams, leading to misalignment, inefficiencies, and unfulfilled expectations.
3. What are the key components of a DevOps pipeline?
- Source Control (e.g., Git)
- CI/CD Tools (e.g., Jenkins, GitLab CI)
- Artifact Repository (e.g., Nexus, Artifactory)
- Containerization (e.g., Docker)
- Orchestration (e.g., Kubernetes)
- Infrastructure as Code (IaC) (e.g., Terraform, Ansible)
- Monitoring and Logging (e.g., Prometheus, ELK Stack)
4. How does Infrastructure as Code (IaC) help in DevOps?
Infrastructure as Code (IaC) is a method of provisioning and automating IT infrastructure through code, ensuring consistency, repeatability, and version control. It eliminates manual errors and enables quick scaling and disaster recovery. In the DevOps ecosystem where seamless and consistent updates are required more often, IaC makes it more reliable than rendering things through manual intervention. During the learning curve some challenges may surface such as complexity in code maintenance, ensuring security and compliance across diverse areas.
5. What is the difference between Terraform and Ansible?
Terraform is an open-source tool, that allows developers to automate the provisioning and management of infrastructure using a declarative configuration language. This means that instead of writing code to tell the system what to do, you define the desired state of your infrastructure, and Terraform handles the details of achieving that state.
Ansible is an open-source automation tool used for managing and configuring and orchestrating IT infrastructure, applications, and services. It allows users to define and enforce desired states for their infrastructure and applications through simple, human-readable configuration files. Ansible is particularly useful for automating repetitive tasks, ensuring consistency across environments, and reducing the risk of human error.
6. What is CI/CD?
Continuous Integration (CI) and Continuous Delivery (CD) are modern software development practices aimed at automating and streamlining the process of integrating, testing, and delivering code changes. These practices encourage developers to frequently commit their code to a central repository (such as GitHub or Bitbucket/Stash), enabling faster and more reliable software releases.
Continuous Integration (CI):
CI involves automatically integrating code changes from multiple developers into a shared codebase. Using a version control system like Git, developers push updates regularly. Each commit triggers an automated CI pipeline that typically performs tasks such as building the code, running unit tests, generating and storing build artifacts, and conducting static code analysis with tools like SonarQube. This process helps catch integration issues early, ensuring code quality and stability. Continuous Delivery (CD):
CD extends CI by automating the deployment process to environments that closely mimic production. This includes running a series of automated tests — such as UI tests, load tests, and integration tests — to validate the application before it goes live. Continuous delivery ensures that code is always in a deployable state and minimizes the risk of unexpected issues after deployment. It empowers development teams to release features and fixes faster, with greater confidence.
7. How would you secure your CI/CD pipeline?
- Use secrets management tools (Vault, AWS Secrets Manager)
- Implement least-privilege access controls
- Code scanning and dependency vulnerability checks
- Use signed commits and artifact integrity verification
- Monitor pipeline activity and audit logs
8. How does a DevOps engineer handle secrets management?
A DevOps engineer must ensure that sensitive information such as API keys, database credentials, and encryption keys is securely managed to prevent unauthorized access and leaks. Proper secrets management involves:
- Employ tools like HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, or Kubernetes Secrets to securely store and manage credentials.
- Never store secrets directly in code repositories, configuration files, or environment variables where they can be exposed accidentally or accessed maliciously.
- Whenever possible, use dynamically generated secrets like short-lived access tokens, that expire after a set duration, reducing the risk of long-term exposure.
- Restrict access to secrets using policies that grant permissions only to necessary users and services.
- Periodically update secrets to reduce exposure risks, using automation tools to seamlessly rotate credentials without manual intervention.
9. What is Blue-Green Deployment?
In DevOps, Blue-Green Deployment and Canary Deployment are two strategies for releasing updates with minimal downtime and risk. Blue-Green Deployment uses two identical environments—Blue (live/stable) and Green (new version). Updates are deployed to Green, tested, and then traffic is switched from Blue to Green. If issues arise, traffic can quickly revert to Blue. Canary Deployment gradually rolls out updates to a small user group (e.g., 1%), increasing the percentage as stability is confirmed. If issues are detected, the update can be rolled back before full deployment.
10. What are some common metrics you monitor in production?
- CPU/memory/disk usage
- Application response time
- Error rates
- Request throughput (TPS)
- Deployment frequency and rollback rate
11. Explain a situation where you automated a process that saved significant time.
Example: Automated the provisioning of staging environments using Terraform and Ansible, reducing setup time from 1 hour to 5 minutes and eliminating human error.
Example: Automated log file rotation and archiving using a Bash script combined with a cron job. This replaced a manual weekly task, saving 4—5 hours per month and ensuring consistent log management across multiple servers.
12. How would you tackle the issue which goes undetected across multiple deployments?
Through Advanced monitoring (e.g., Prometheus, Grafana, ELK stack) and observability tools (e.g., OpenTelemetry, Datadog) continuously track application health, logs, and metrics. These tools help quickly detect anomalies when that rare condition is finally triggered.
Also features can be toggled on/off without redeployment. If an issue arises, the problematic feature can be disabled instantly without affecting the rest of the system.
Deployment pipelines (e.g., in Jenkins, GitHub Actions, or Argo CD) often include rollback mechanisms that can restore a previous stable version when a failure or anomaly is detected in production.
13. How would you handle rollback in Kubernetes?
Use
kubectl rollout undo deployment/
14. What tools do you use for monitoring and logging?
- Monitoring: Prometheus, Grafana, Datadog
- Logging: ELK Stack (Elasticsearch, Logstash, Kibana), Fluentd, Loki
- Alerting: Alertmanager, OpsGenie, PagerDuty
15. What is the role of Containers in DevOps?
Containers are the core components and play a vital role in DevOps by revolutionizing the way applications are developed, deployed, and managed. They encapsulate applications and their dependencies into portable, self-sufficient units that can run uniformly across different computing environments, whether it’s a developer’s local machine, a testing server, or a production environment. This ensures consistency and minimizes issues arising from environmental discrepancies.
In the context of CI/CD (Continuous Integration and Continuous Deployment), containers simplify the pipeline by providing standardized environments for building, testing, and deploying applications. They reduce the complexity of dependency management and ensure that the software behaves predictably across stages of the pipeline. Their isolation capabilities ensure that issues in one container do not impact others, enhancing reliability and fault tolerance.
16. What is the role of Kubernete in DevOps?
Kubernetes (often abbreviated as K8s) is an orchestration tool for managing containerized applications at scale. It automates deployment, scaling, and management of containers, ensuring high availability and efficient use of resources. Kubernetes helps DevOps teams handle complex infrastructures by simplifying load balancing, monitoring, and self-healing mechanisms. Together, Docker and Kubernetes form a powerful duo, enabling DevOps teams to build, deploy, and manage complex applications more efficiently in modern cloud-native environments.
17. What is the difference between Horizontal and Vertical Scaling?
Horizontal Scaling
Horizontal scaling involves increasing the number of servers or machines to manage growing workloads. Rather than enhancing the capacity of a single server, multiple servers are employed to distribute the workload. It’s comparable to adding more lanes on a highway by widening the road to allow for more traffic simultaneously. This method is particularly effective for accommodating higher traffic or a greater number of users, as servers can be added incrementally as demand rises. Additionally, horizontal scaling provides enhanced reliability, as the failure of one server is unlikely to disrupt the system entirely. However, implementing and maintaining a network of servers can be more complicated, often requiring load balancers to evenly distribute traffic across the servers.
Vertical Scaling
Vertical scaling, on the other hand, focuses on enhancing the capabilities of a single server. This is achieved by upgrading its hardware, such as adding more RAM, a faster CPU, or larger storage. Think of it as boosting the performance of your personal computer by upgrading its components. Vertical scaling is straightforward to implement and manage, as it involves just one server. It works well for smaller applications or systems with stable and predictable traffic patterns. However, there are physical and technical limits to how much a single server can be upgraded. Additionally, performing such upgrades may require restarting the server, leading to a brief downtime. By understanding these two approaches, organizations can choose the most suitable scaling strategy based on their specific needs and resources. Sometime when the business grows, a hybrid approach is adopted to fully optimize the resources and their availability.
18. What happens in a Typical CI/CD Pipeline?
- 1. Code Commit: Developer saves code to a shared place (like GitHub). Typical tools are Git, GitHub, GitLab
- 2. Build: Code is compiled or packaged. Typical tools are Maven, Gradle, npm
- 3. Automated Testing: Run pre-written test scripts to check for bugs. Typical tools are JUnit, Selenium, Cypress
- 4. Integration: Ensure the new code works well with existing code. Typical tools are Docker, Kubernetes
- 5. Deploy: Release the app to staging or production servers. Typical tools are Jenkins, GitLab CI, AWS CodeDeploy
- 6. Monitor: Keep an eye on performance or crashes after deployment. Typical tools are Prometheus, Grafana, ELK Stack
19. How do you debug failing deployments in CI/CD?
Check pipeline logs, test output, and commit diffs. Roll back changes if necessary. Use verbose logging and isolate variables through step-by-step testing.
20. What’s the difference between Stateful and Stateless applications in Kubernetes?
Stateless applications
A stateless application does not retain any data or session information about a user or task once the process is completed or the application is restarted. Each request is treated independently. Stateless application has following key characteristics:
- No memory of previous interactions
- Easily scalable (can add/remove pods quickly)
- Can be rescheduled or restarted on any node without issue
- Great for workloads like web front-ends, APIs, microservices, etc.
- Typically deployed using a Deployment or ReplicaSet.
- No need for persistent storage.
- Load balancers can freely distribute traffic to any pod.
- Examples include but not limited to NGINX web servers, RESTful APIs, Front-end Microservices
Stateful applications
A stateful application retains information about past interactions or maintains data/state across sessions. These applications often depend on persistent storage, consistent network identity, and ordered startup/shutdown. Stateful application has following key characteristics:
- Retains state/data (user sessions, files, configurations)
- Requires stable identities (like pod names or IPs)
- Needs persistent storage that remains even if the pod dies
- Can’t just scale freely like stateless apps; requires thoughtful design
- Deployed using StatefulSets, which:
- Use PersistentVolumeClaim (PVC) for storage
- PVCs are bound to each pod and do not automatically shift
- Examples include but not limited to Databases like MySQL, PostgreSQL, MongoDB, Apache Kafka, Zookeeper, Any app where data persistence is critical
21. How do you ensure security and compliance in a CI/CD pipeline, specifically while integrating with multiple cloud providers and third-party services?
To ascertain security and compliance in the CI/CD pipeline, which integrates multiple cloud providers and third-party services:
- Implement strong authentication and authorization: Secure access to resources.
- Use encryption: Protect data during transmission and storage.
- egularly audit access controls: Ensure compliance and security.
- Automate security scanning and testing: Identify vulnerabilities early.
- Maintain clear documentation and communication: Stay updated on compliance requirements.
22. What is the use of service meshes like Istio or Linkerd in DevOps?
Service meshes like Istio and Linkerd are used to manage and control the communication between microservices in a distributed application architecture. In modern DevOps environments where microservices are frequently deployed across containers and orchestrated by platforms like Kubernetes, a service mesh helps solve several challenges related to service-to-service communication without requiring changes in application code. Key capabilities of a service mesh include:
- Traffic Management (Traffic Shaping): Allows fine-grained control over how requests are routed between services. You can implement canary deployments, blue/green deployments, A/B testing, and progressive rollouts using custom routing rules.
- Retries, Timeouts, and Circuit Breaking: These resilience features improve service reliability by automatically retrying failed requests, enforcing timeouts to avoid hanging services, and stopping requests to unhealthy services temporarily to prevent cascading failures.
- Authentication and Authorization: Service meshes support mutual TLS (mTLS) to encrypt traffic between services and verify the identity of communicating services, ensuring secure service-to-service communication.
- Observability: Tools like Istio and Linkerd provide built-in telemetry, logging, tracing, and monitoring. They integrate with observability tools (like Prometheus, Grafana, Jaeger, etc.) to help DevOps teams understand traffic patterns, debug issues, and optimize performance.
- Policy Enforcement: You can define and enforce policies around rate limiting, quotas, and access control without modifying application code.
23. What is a GIT Repository?
A Git repository is a version control system that tracks changes to files and directories, allowing developers to collaborate efficiently. For DevOps operations, Git repositories play a crucial role in automating software development and deployment. A Version Control System (VCS) is used to create and maintain versioning of the files. There are two types of repositories in Git:
- Local Repository — Stored on a developer’s local machine, where changes are made before they are pushed.
- Remote Repository — Hosted on platforms like GitHub, GitLab, or Bitbucket, enabling team collaboration.
Key features of a Git repository include:
- Branching & Merging — Developers can create branches to work on different features simultaneously.
- Commit History — Maintains a log of all changes, making rollback and tracking easy.
- Collaboration & Automation — Essential for CI/CD workflows in DevOps, integrating seamlessly with tools like Jenkins, Docker, and Kubernetes.
24. What is Git stash?
Git stash is a handy feature in Git that allows you to temporarily save changes in your working directory without committing them. This is useful when you’re in the middle of working on something but need to switch branches or pull updates without losing your current work. When you run git stash, Git takes all your uncommitted changes and stores them in a “stash stack.” You can later apply those changes back to your working directory using git stash apply or git stash pop (which applies and removes the stash from the stack). It’s great for keeping your workspace clean while working on multiple tasks.
25. How is AI being integrated into DevOps, and what role can it play in enhancing software quality and minimizing issues across different stages of DevOps lifecycle?
AI is increasingly being integrated into DevOps workflows under the umbrella of AIOps (Artificial Intelligence for IT Operations). Its key contributions include:
- Automated anomaly detection: AI can analyze logs and metrics in real-time to identify performance issues or failures before they impact users.
- Predictive analytics: By learning from historical data, AI models can forecast potential system failures, allowing proactive measures.
- Intelligent test automation: AI can optimize testing by identifying high-risk areas in code changes and prioritizing test cases accordingly.
- Root cause analysis: AI-driven tools can correlate events and logs to suggest or even pinpoint the cause of incidents faster than manual investigation.
- Smart CI/CD pipelines: AI can decide when to trigger builds/tests based on code change patterns, or even halt a deployment if quality gates are likely to fail.
- Capacity planning and auto-scaling: AI models can predict resource demands and optimize infrastructure usage dynamically.
This not only reduces Mean Time to Resolution (MTTR) but also ensures higher reliability and faster feedback loops.
