Crack SDET Interview in 15 Minutes | Ultimate Preparation Guide For Beginners

About The Video

Preparing for your first SDET interview? This Episode 1 of the SDET Interview Masterclass breaks down essential beginner questions, AI-enhanced practices, and real-world automation strategies for 2025.

𝐓𝐨𝐩 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝

✔ What Is the Role of an SDET in Today’s AI-Augmented Development Teams?

✔ How Is an SDET Different From a Traditional QA Engineer in 2025?

✔ What Core Skills and Tools Should a Modern SDET Master Have to Stay Relevant?

✔ How Do You Decide What to Automate and What Not to Automate?

✔ Can You Share a Real-World Example of Catching a Critical Defect Before Release?

✔ How Does Generative AI Change the Way SDETSs Work Today?

✔ How Do You Collaborate Effectively With Developers, DevOps and the Product Team?

Key Topics Covered

Introduction to SDET Role in 2025:

SDETs go beyond traditional QA roles; they write automated test scripts, build frameworks, integrate with CI/CD, and influence quality from the design phase.

Act as quality advocates in Agile teams, ensuring testing is not an afterthought.

Difference Between SDET and QA Engineer:

QA engineers focus on manual testing, exploratory testing, and bug logging.

SDETs write automation scripts, create frameworks, integrate with CI/CD, and manage DevOps aspects for scalable testing.

SDETs leverage AI to generate and optimize test scripts but still validate code for efficiency and maintainability.

Core Skills and Tools for Modern SDETs:

Programming: Java, Python, JavaScript/TypeScript with OOP concepts.

Automation Frameworks: Selenium, Robot Framework, Playwright, Cypress, and AI-assisted tools.

CI/CD & DevOps: Jenkins, GitHub Actions, GitLab CI, monitoring tools (Datadog, Grafana, Prometheus).

API & DB Testing: Postman, RestAssured, Robot Framework APIs.

Cloud Infrastructure: AWS, GCP, Azure, OCI.

Non-functional Testing: Performance (JMeter, Locust), Frontend metrics (Lighthouse, GTMetrix), Security (OAS, ZAP, Burp Suite).

Automation Strategy:

Use risk-based prioritization: automate critical business flows first (login, payments, checkout).

Separate tests into smoke tests (fast, high-priority, run on every commit) and regression tests (broader coverage, run nightly or pre-release).

Apply test impact analysis to run only affected tests, reducing build times significantly.

Real-world Example:

Detected a high-risk defect in an e-commerce checkout flow using automated tests and CI alerts, preventing revenue loss before production.

AI in Testing:

AI aids in locator healing, script generation from plain English, edge-case test data creation, and maintaining outdated scripts.

Human review is essential; AI amplifies SDET work but does not replace decision-making.

Future trend: agentic AI that autonomously runs, interprets, and fixes tests, while current era is hybrid AI-human collaboration.

Collaboration with Teams:

Work with developers: sprint planning, pull request reviews, shift-left testing.

Work with DevOps: integrate automation into CI/CD, containerization, ephemeral environments, and automated quality gates.

Work with product teams: translate acceptance criteria into tests, provide risk-based insights, maintain shared quality dashboards.

Conclusion and Next Steps:

Key takeaways: SDETs are now engineers, AI collaborators, and quality partners, not just testers.

Upcoming topics: automation frameworks, API testing, flaky test mitigation, CI/CD integration, and advanced AI usage.