SDET Interview Guide (Intermediate) | Automation & Frameworks

About The Video

After covering the fundamentals in Episode 1, we’re stepping into the intermediate level, where interviewers expect more than just theory. This episode dives deep into framework-driven, automation-heavy interview prep with real-world problem-solving approaches.

𝐓𝐨𝐩 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝

✅ How would you handle dynamic elements in modern automation tools like Playwright or Cypress?

✅ What is the Page Object Model (POM)? Is it still relevant, and how is it evolving in 2025?

✅ How would you implement data-driven testing in your automation framework?

✅ How do you test REST APIs and integrate them into your automation framework?

✅ What’s your approach to version control and branching strategies for automation code?

✅ How can AI improve test case coverage and maintenance?

Key Topics Covered

Introduction to SDET Role in 2025:

The session, part of the SDET Interview Masterclass series powered by LambdaTest, builds on the fundamentals covered in episode one and takes viewers into more technical, framework-driven, and automation-heavy territory.

The episode begins by tackling one of the most common challenges in UI automation dynamic elements. Siddhant explains how to handle changing IDs, flaky locators, and instability in frameworks like React, Angular, or Vue. He stresses the importance of using stable semantic locators such as data-test-id, aria-labels, or visible text, and leveraging role or text-based locators in Playwright and Selenium.

He further advises building resilience into locators with explicit waits, fallback strategies, and AI-assisted healing. Pro tips include maintaining a glossary mapping business actions to selectors and avoiding blind sleeps to make test execution more reliable.

Next, the video dives into the Page Object Model (POM). While some question its relevance in modern testing, Siddhant emphasizes that POM is still valuable but has evolved. He demonstrates best practices such as creating component-based POMs for micro frontends, leveraging TypeScript for type safety, keeping methods small and declarative, and centralizing locators for maintainability.

AI-generated scaffolding can accelerate setup, but human refactoring remains essential. The key takeaway is to keep POMs deterministic, stateless, and focused for easier parallel execution and long-term maintainability.

The discussion then moves into data-driven testing, highlighting how separating test data from logic allows scalable and reusable tests. Siddhant demonstrates fetching data from local JSON or CSV files, remote stores like S3, APIs, or even generating synthetic data with tools like Faker or AI. Using Playwright examples, he shows how iterating over JSON data sets reduces duplication, improves test scalability, and simplifies maintenance. He also shares orchestration techniques such as seeding environments with Terraform and SQL, parameterizing test data paths in CI pipelines, and applying test impact analysis to avoid unnecessary test explosions.

Another key focus area is API testing. Siddhant explains a layered approach: schema and contract validation with Swagger or OpenAPI, functional tests for response codes and payloads, negative testing, and non-functional checks like performance, latency, and concurrency with tools like JMeter or K6. He stresses the importance of running API tests early as part of shift-left testing, integrating them into CI pipelines, and using dashboards for traceability. Pro tips include generating mocks from OpenAPI specs to enable frontend and backend teams to work independently.

The episode also covers version control and branching strategies for automation code. Siddhant treats automation scripts as first-class citizens in the SDLC, advocating Gitflow for large teams and trunk-based development for fast-paced CI/CD projects. He explains best practices such as frequent small commits with meaningful messages, PR templates with automation-specific checklists, peer code reviews, and branch protection rules that require CI checks before merges. Tagging stable automation releases with semantic versioning ensures traceability across application and test codebases. He also advises isolating AI-generated scripts in separate branches for review before merging.

Finally, Siddhant explores how AI is transforming test case coverage and maintenance in 2025. Advanced AI techniques now provide intelligent gap analysis by scanning code changes and production logs to identify untested flows, mine user behavior analytics to generate high-risk coverage scenarios, and apply model-based testing to map DOM structures for automated test creation. AI also improves maintenance with self-healing locators, impact-based test selection that reduces CI runtime, and natural language test updates. While AI accelerates speed and scalability, he stresses the importance of human oversight, training AI on historical defect data, and maintaining audit logs for compliance.

The video concludes by summarizing the must-know skills that separate beginners from confident mid-level automation engineers: building robust locators, structuring maintainable page objects, implementing scalable data-driven tests, integrating APIs effectively, applying disciplined version control strategies, and leveraging AI to improve both coverage and efficiency. Siddhant closes by teasing the next advanced-level episode, which will explore scalable design patterns, performance testing, test flakiness handling, and deeper AI integration into DevTestOps pipelines.