ISTQB CT-AI: Certified Tester AI Testing with Practice Tests

Prepare for ISTQB CT-AI Certification with complete AI Testing concepts, practice questions & exam readiness tips.

What you'll learn
- Master the entire ISTQB CT-AI syllabus, from fundamental AI concepts to advanced testing techniques.
- Understand the critical differences between testing traditional software and complex AI systems.
- Apply powerful testing techniques like Metamorphic Testing, Adversarial Testing, and Pairwise Testing.
- Confidently prepare for and pass the CT-AI certification exam with our specially curated practice tests
- Develop job-ready skills for AI testing roles through unique, AI-powered role-play activities.

Requirements
- A foundational understanding of software testing principles and terminology is required.
- A desire to learn and future-proof your career in software quality!

Description
Disclaimer:This course integrates Artificial Intelligence under the guidance of an experienced professional with extensive expertise in the subject matter.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming how software is built and tested. TheISTQB Certified Tester – AI Testing (CT-AI)certification validates your ability to apply structured testing approaches to AI-driven systems. This course is designed to give you the knowledge, practice, and confidence topass the ISTQB CT-AI exam in 2025and advance your career in software testing.

Created byAnup Naik(MSc in Artificial Intelligence, 15+ years in software testing), this crash course blends focused lessons, role-play scenarios, and realistic practice tests to prepare you for both the exam and real-world AI QA challenges.

What You’ll Learn

Core CT-AI concepts: metamorphic testing, concept drift, test oracle problems, bias, adversarial attacks, explainability , neural network coverage

How to test AI-based systems across the lifecycle – from data preparation to deployment

AI-specific quality characteristics, risks, and ethical considerations

Practical strategies for AI QA: test environments, performance metrics, defect detection

Role-play activities to simulate real-world AI testing projects and interview scenarios

Syllabus Coverage (aligned with ISTQB CT-AI 2025):

Introduction to AI and ML fundamentals

Quality characteristics of AI-based systems

ML data, metrics, and neural networks testing

Testing AI-specific quality characteristics

Methods and techniques for AI system testing

Test environments for AI-based systems

Using AI for testing and automation

Practice Tests and Exam Prep

150+ exam-style questions with detailed explanations

Difficulty levels mapped (easy/medium/hard)

Performance analytics and topic-wise scoring

Time management insights for exam readiness

Who Should Enroll

Professionals preparing for the ISTQB CT-AI certification exam

QA testers, engineers, and automation specialists moving into AI/ML testing

Team leads, managers, and tech enthusiasts seeking AI QA expertise

Key Benefits

Fully aligned with ISTQB CT-AI 2025 syllabus

Role-play activities for job-ready AI testing skills

Lifetime updates as the syllabus evolves

In-depth feedback with every practice test

Builds both exam confidence and career skills

Important Note

Role-play activities work best on laptop or desktop. They are not supported on mobile or tablet devices.

Disclaimer

This is an unofficial course. It is not affiliated with or endorsed by ISTQB or its member boards. The course does not contain actual exam questions or dumps. “ISTQB” is a registered trademark of the International Software Testing Qualifications Board. For official exam details, please visit the ISTQB website.

Who this course is for:
- Software Testers, QA Engineers, and Test Analysts looking to upgrade their skills.
- Test Leads and Test Managers planning to implement AI testing strategies.
- Anyone preparing for the official ISTQB Certified Tester AI Testing (CT-AI) certification.
- Software Developers who want to understand how to build and test robust AI applications.
- Business Analysts and Product Owners involved in defining requirements for AI systems.
More Info