AI Testing Academy
Your AI-Powered QA Career Launchpad

Master test automation, DevOps, and AI testing with hands-on agents, structured lectures, and real interview practice. Built for QA engineers who want to stay ahead of the curve.

Try a sample interview → Analyze a sample CV Watch Lectures
What you get:
  • Agent 1 — AI resume scorer and rewriter for QA roles
  • Agent 2 — Live mock interview, 5 stages
  • Lecture Series — 10 in-depth lectures on AI Testing
  • Question Bank — 25+ real interview questions + AI enrichment

Agent 1 — Resume & Cover Letter

Upload your CV and get a scored evaluation with strengths, gaps, and an AI-rewritten version tailored to any QA or SDET role.

🎓 Lecture Series

Two structured tracks of 10 in-depth lectures each — from AI testing fundamentals to advanced evaluation techniques, and from AI basics to foundational cybersecurity practice. Work through them in order, or jump to what you need most.

AI Testing

From AI testing fundamentals to advanced evaluation techniques.

  1. Lecture 1 — Introduction to AI Testing (Live)

    What is AI testing, why it matters, and how it differs from traditional software testing. Covers LLMs, non-determinism, evaluation strategies, and the modern AI testing landscape.

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  2. Lecture 2 — Prompt Engineering for Testers (Live)

    How to write prompts that produce consistent, testable outputs. Covers prompt structure, system messages, temperature, and prompt injection basics.

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  3. Lecture 3 — Testing LLM Outputs (Live)

    Evaluation frameworks for LLM responses — semantic similarity, factuality checks, toxicity detection, and JSON schema validation.

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  4. Lecture 4 — Playwright for AI Applications (Live)

    End-to-end testing of AI-powered UIs with Playwright — handling dynamic content, testing streaming responses, and building resilient selectors.

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  5. Lecture 5 — API Testing with AI Features (Live)

    Testing AI APIs with pytest and Requests — mocking LLM responses, testing edge cases, and validating structured outputs.

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  6. Lecture 6 — CI/CD for AI Test Suites (Live)

    Running AI tests in GitHub Actions — parallelism, flakiness handling, cost management, and integrating LLM-as-judge into pipelines.

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  7. Lecture 7 — Security Testing for AI (Live)

    Prompt injection attacks, data leakage, jailbreaking, and adversarial testing. How to write security tests for LLM-powered features.

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  8. Lecture 8 — Performance Testing AI Features (Live)

    Latency benchmarking, throughput testing, and token-cost optimization. Load testing AI endpoints and establishing performance baselines.

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  9. Lecture 9 — AI-Assisted Test Generation (Live)

    Using AI agents to generate test cases, identify edge cases, and triage failures. GitHub Copilot, Cursor, and custom test-generation pipelines.

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  10. Lecture 10 — Building an AI Testing Strategy (Live)

    Putting it all together — designing a full AI testing strategy for your team, from unit to system level, with metrics, reporting, and continuous improvement.

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AI-Powered Basic Cybersecurity

Foundational cybersecurity concepts, powered and accelerated by AI tools.

  1. Lecture 1 — Introduction to Cybersecurity with AI (Live)

    The fundamentals of cybersecurity — threats, attack surfaces, and defense-in-depth — and how AI tools are reshaping how security teams work today.

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  2. Lecture 2 — AI in Network Security Monitoring (Live)

    Applying AI to traffic analysis and intrusion detection — spotting lateral movement and exfiltration patterns in network logs.

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  3. Lecture 3 — AI-Powered Threat Detection Basics (Coming soon)

    How machine learning models flag suspicious activity, from rule-based detection to anomaly scoring, and where AI adds real value over static rules.

  4. Lecture 4 — Phishing & Social Engineering Detection with AI (Coming soon)

    Using AI to spot phishing emails, deepfake voice scams, and social engineering attempts before they reach end users.

  5. Lecture 5 — Malware Analysis Using Machine Learning (Coming soon)

    Classifying and clustering malware samples with ML models, and the basics of static vs. behavioral analysis pipelines.

  6. Lecture 6 — Automating Vulnerability Scanning with AI (Coming soon)

    How AI-assisted scanners prioritize and triage vulnerabilities, reducing noise and helping teams focus on what matters.

  7. Lecture 7 — AI for Log Analysis & Anomaly Detection (Coming soon)

    Turning massive log volumes into actionable alerts with AI-driven anomaly detection and clustering techniques.

  8. Lecture 8 — Securing AI Systems Themselves (Coming soon)

    The flip side — protecting your own AI systems from prompt injection, model theft, data poisoning, and supply-chain risks.

  9. Lecture 9 — AI-Assisted Incident Response (Coming soon)

    Using AI copilots to speed up triage, root-cause analysis, and reporting during a live security incident.

  10. Lecture 10 — Building a Cybersecurity AI Strategy (Coming soon)

    Putting it all together — a practical roadmap for adopting AI across detection, response, and prevention in your security program.

Agent 2 — Mock Interview

Run a realistic QA-Automation interview with an AI interviewer — five stages, from HR to AI testing.

❓ Real Interview Questions

The questions QA-Automation candidates actually get, grouped by the five interview stages. Click a question for a hint, click again for a full answer, then run a live mock with the agent above.

🐍 Python Coding Challenges for Test Automation

40 practical coding problems that come up in real QA-Automation interviews, grouped into three levels. Click once to reveal a hint, click again to reveal a short, efficient solution with time and space complexity.

Level 1 — Fundamentals

Lists, dicts and a little recursion. Nothing here needs a clever trick, only a clean single pass.

  1. 1. Deduplicate test IDs, preserving order
  2. 2. Find the first duplicate test ID
  3. 3. Flatten a nested test suite
  4. 4. Count test results by status
  5. 5. Find the N slowest tests
  6. 6. Group tests by tag
  7. 7. Chunk test IDs into batches
  8. 8. Parse a duration string into seconds
  9. 9. Diff two config dicts
  10. 10. Which tests ran yesterday but not today?
  11. 11. Find the longest test name
  12. 12. Total and average suite duration
  13. 13. Compute the pass rate
  14. 14. Turn a test title into a valid identifier
  15. 15. Zip names and statuses into a dict
  16. 16. Tests that failed in every run
  17. 17. Sort tests by status, then by duration
  18. 18. Drop empty query parameters
  19. 19. Find gaps in a numbered test sequence
  20. 20. Case-insensitive search over test names

Level 2 — Interview standard

The shape most QA-Automation interviews actually take: decorators, polling, schema validation and log diffing.

  1. 1. Poll until a condition is true
  2. 2. Retry decorator for flaky tests
  3. 3. Validate an API response shape
  4. 4. Diff two test-run logs
  5. 5. Split tests into balanced CI shards
  6. 6. Detect flaky tests across runs
  7. 7. Deep-merge config with environment overrides
  8. 8. Redact secrets from log lines
  9. 9. Assert an API response contains an expected subset
  10. 10. Walk a paginated API endpoint

Level 3 — Advanced

Where candidates get separated: intervals, caching, graphs and concurrency.

  1. 1. Merge overlapping CI job time ranges
  2. 2. Rate-limit an API test helper
  3. 3. Cache an expensive test fixture (LRU)
  4. 4. Detect a cycle in fixture dependencies
  5. 5. Run async tests with a concurrency limit
  6. 6. Order fixture setup by dependency
  7. 7. Run blocking health checks in a thread pool
  8. 8. A context manager with guaranteed cleanup
  9. 9. Bisect the build that broke a test
  10. 10. Top-K error messages in a huge log

Connection Setup

Connect your AI provider once — both agents share the same key. Gemini has a free tier; Claude gives the best resume rewrites.