AI Quality Analyst Course & Curriculum

NCPL's hands-on AI Quality Analyst training — full curriculum, real projects, certification prep, and job placement support. We train, mentor and place you.

Module 1: Software Testing & QA Foundations

Master core QA principles, SDLC/STLC, and test design techniques

  • SDLC, STLC, and Agile/Scrum testing
  • Test design: BVA, equivalence partitioning, decision tables
  • Functional, regression, smoke, and sanity testing
  • Test plans, test cases, and defect lifecycle
  • Bug tracking with Jira, Azure DevOps, TestRail
  • API testing fundamentals with Postman
  • ISTQB Foundation aligned content

Module 2: Test Automation & Performance

Hands-on automation with Selenium, Playwright, and CI/CD integration

  • Python / Java fundamentals for testers
  • Selenium WebDriver and Playwright
  • API automation with REST Assured / Postman / Newman
  • BDD with Cucumber and Gherkin
  • Performance testing with JMeter / k6
  • CI/CD integration: GitHub Actions, Jenkins
  • Cross-browser and mobile testing basics

Module 3: AI, ML & GenAI for Quality Assurance

Apply AI, LLMs, and ML models to accelerate and enhance testing

  • AI/ML fundamentals for QA professionals
  • Prompt engineering with ChatGPT, Claude, Gemini
  • GenAI for test case, test data & script generation
  • Self-healing test automation with AI tools
  • Visual & UI testing with Applitools / Percy
  • AI-powered defect prediction and triage
  • Testing LLM-based applications: prompt, hallucination, safety
  • Bias, fairness, and responsible AI testing

Module 4: AI Quality Engineering & Delivery

End-to-end quality strategy for AI-enabled products

  • Quality strategy for AI/ML systems
  • Data quality, drift, and model monitoring
  • MLOps awareness and test environments
  • Security & privacy testing for AI apps
  • Test metrics, dashboards, and reporting
  • Capstone: AI-augmented test framework + LLM app testing
  • Resume, portfolio, and interview preparation