API testing is a type of software testing that verifies whether APIs — the layer connecting the UI, server, and database — work correctly in terms of functionality, reliability, performance, and security. Instead of clicking through screens, you send direct requests to endpoints and validate the status code, response body, headers, and response time. For example, sending a POST request to a login API should return 200 with a valid token for correct credentials and 401 for wrong ones.

Start with the fundamentals — HTTP methods, status codes, JSON, and how client-server communication works. Then practice on free public APIs using Postman: send GET/POST requests, add headers and authentication, and write basic assertions. Once manual testing feels comfortable, move to automation with tools like REST Assured or Playwright and learn how API tests fit into CI/CD pipelines. Consistent hands-on practice on real APIs matters far more than only watching tutorials.

Create a new request, choose the HTTP method, enter the API endpoint, and add any required headers, query params, authentication, or request body. Click Send and validate the response — status code, response time, and JSON data. Then save your requests into collections and environments so they can be reused and executed in bulk. Postman is the most beginner-friendly of all API testing tools, which is why most testers start there before moving to REST Assured or JMeter.

Write assertions in the Scripts/Tests tab using pm.test — for example, checking the status code, response time, or specific JSON fields. Organize requests into collections, use environments for test and staging URLs, and run everything with the Collection Runner or Newman (Postman's CLI). For full automation, integrate Newman into Jenkins or GitHub Actions so your API suite runs on every build or pull request.

Frequently asked ones include: difference between PUT and POST, meaning of status codes (200, 201, 400, 401, 403, 404, 500), how authentication works (Basic Auth, Bearer tokens), how to validate JSON responses, how to chain requests using data from one response in another, and API testing vs UI testing. Always prepare answers with a real example from your project — interviewers care more about how you tested and what bugs you caught than textbook definitions.

Search Naukri, LinkedIn, and Indeed with combinations like "API testing", "automation testing", or "SDET" along with Pune, and set daily alerts since good openings fill quickly. Keep your resume ATS-friendly with keywords such as Postman, REST Assured, API automation, and CI/CD, build two or three hands-on projects you can demo, and use referrals and testing communities — companies in Pune's IT hubs like Hinjewadi and Kharadi hire actively for QA automation roles.

Expect questions on CI/CD fundamentals, Docker vs virtual machines, Kubernetes basics, Jenkins pipeline stages, Git branching strategies, infrastructure as code, monitoring, and troubleshooting a failed deployment. Many candidates revise from a DevOps interview questions and answers PDF the night before, but the answers that get selected explain the "why" with a real scenario from your own projects.

Fresher interviews stay close to fundamentals: what DevOps is and why it exists, basic Linux commands, Git operations, a simple CI/CD flow, Docker image vs container, and basic networking like DNS, HTTP, and ports. Be ready to walk through one small hands-on project — even a basic Jenkins or GitHub Actions pipeline that builds and tests a sample app — because freshers are judged on practical curiosity more than depth.

These are "what would you do if..." questions — a deployment failed midway, the pipeline suddenly slowed down, a container keeps crashing in production, secrets got exposed, or you need a zero-downtime rollback. Strong answers follow a clear flow: check monitoring and logs, isolate the cause, fix or roll back, and add a safeguard so it doesn't repeat. Since most DevOps interview questions for 3 years experience and above are scenario-based, practice narrating one real incident end-to-end instead of memorizing tool definitions.

Be prepared for YAML vs classic pipelines, Microsoft-hosted vs self-hosted agents, service connections, variable groups and secret handling, approvals and gates, artifact feeds, boards, and branch policies. Interviewers often open with basics like "What is a CI/CD pipeline in Azure?" or "How is Azure DevOps different from Jenkins?", so structure your answers around a build-and-release flow you have actually worked with.

CI stands for Continuous Integration — merging code changes frequently and verifying each change with an automated build and tests. CD stands for Continuous Delivery or Continuous Deployment — either keeping every release ready to ship on demand or pushing every change that passes tests straight to production. Together, they describe the automated path code takes from a developer's commit to a live release.

It is the automated workflow that carries every code change from commit to production — typically build, automated tests, security scans, artifact creation, deployment, and monitoring. It removes manual handoffs, so teams release faster with fewer errors. Common CI/CD pipeline tools include Jenkins, GitHub Actions, Azure DevOps, GitLab CI/CD, and CircleCI, usually combined with Docker and Kubernetes for building and deploying applications.

The usual set covers the meaning of CI/CD, stages of a pipeline, difference between continuous delivery and continuous deployment, declarative vs scripted Jenkins pipelines, how builds are triggered, handling a failing stage, and blue-green vs canary deployments. The answers that stand out describe a pipeline you have personally built or simulated, including one problem you debugged in it.

Start simple: keep your code in Git, add a build step, attach automated tests, publish an artifact, deploy to a staging environment, and only then add a production stage with manual approval. Many teams create a CI/CD pipeline using Jenkins with a Jenkinsfile, while repo-hosted options like GitHub Actions or Azure Pipelines are easier for beginners. Get one small project running end-to-end first, then layer on code quality checks, security scans, and smarter deployment strategies.

Use GitHub Actions — add a YAML workflow file under .github/workflows, define the trigger (push or pull request), and add jobs and steps for building, testing, and deploying your application. Store credentials as GitHub secrets, use environments for staging and production approvals, and monitor each run from the Actions tab. For a simple web app, a working pipeline can be up and running in under an hour.