AI+ Quality Assurance PractitionerTM
Apply AI and machine learning to software testing: test automation, defect prediction, performance and security testing, NLP for QA, and continuous testing.
Test smarter with AI
- AI Testing Mastery:
Hands-on work with AI-powered testing tools and techniques - Intelligent Automation Edge:
Streamline defect detection and performance testing - QA Career Fast-Track:
Industry-aligned certification to advance a QA career - Languages:
English and Spanish
Prerequisites
- Basic Python and familiarity with the software testing lifecycle
- Basic QA principles and practices
- Foundational machine learning knowledge (beneficial, not required)
The AI+ AI+ Quality Assurance Practitioner™ self-paced course includes:
1. 40 hours of on-demand videos, an e-book, and podcasts.
2. Modular quizzes to help learners monitor their progress.
The course allows learners to study at their own pace. After completing the course, learners can take the certification exam online through a secure remote-proctored system.
Why This Certification Matters?
- Advanced QA skills Integrate AI and ML to automate tasks, predict defects, and optimize performance.
- Testing efficiency and accuracy Use AI tools to speed up defect detection and reduce manual errors.
- Competitive market Build in-demand AI skills that meet industry standards.
- Future-proof career Learn NLP and defect prediction for long-term QA growth.
- Real-world application Practical experience tackling complex QA challenges.
What You'll Learn?
QA overview
Introduction to AI in QA
QA metrics and KPIs
Use of data in QA
AI fundamentals
Machine learning basics
Deep learning overview
Introduction to large language models
Test automation basics
AI-driven test case generation
Tools for AI test automation
Integration into CI/CD pipelines
Defect prediction techniques
Preventive QA practices
AI for risk-based testing
Case study: defect reduction with AI
Basics of NLP
NLP in QA
LLMs for QA
Case study: NLP for bug triaging
Performance testing basics
AI in performance testing
Visualizing performance metrics
Case study: AI performance testing of a cloud app
Exploratory testing with AI
AI in security testing
Case study: enhancing security testing with AI
Continuous testing overview
AI for regression testing
Use case: risk-based continuous testing
AI for predictive analytics in QA
AI for edge cases
Future trends in AI and QA
Job Roles & Industry Outlook
AI Quality Assurance Engineer
Manages AI-based automation strategies to improve testing accuracy and scalability.
NLP QA Specialist
Uses NLP for bug triaging, test case generation, and team communication.
Test Automation Engineer
Implements AI-driven test cases and integrates AI tools into CI/CD pipelines.
Defect Prediction Specialist
Applies AI and ML to predict and prevent defects.
Skills You’ll Gain
Tools You’ll Master
Exam Details
Duration
Passing Score
Format
Delivery Method
Exam Blueprint
What’s Included
(One-Year Subscription + All Updates)
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone