Learning Objectives
After completing this course you will be able to:
- Explain the fundamental concepts of Artificial Intelligence and Machine Learning.
- Differentiate between AI-based systems and conventional software systems.
- Identify AI-specific quality characteristics and quality risks.
- Understand machine learning workflows and model development processes.
- Interpret and apply ML functional performance metrics.
- Perform input data testing, including bias and representativeness testing.
- Select and apply appropriate testing techniques for AI-based systems.
- Test machine learning models throughout their lifecycle.
- Evaluate Generative AI systems and Large Language Models.
- Apply red teaming, adversarial testing and metamorphic testing techniques.
- Contribute to an effective test strategy for AI-based systems.
- Understand the impact of regulations and standards on AI testing.
- Prepare effectively for the ISTQB® Certified Tester AI Testing (CT-AI) examination.
ISTQB® Certified Tester AI Testing (CT-AI)
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being applied in business-critical systems across many industries. While these technologies create significant opportunities, they also introduce new quality risks and testing challenges that cannot be addressed using traditional testing approaches alone. Data quality, bias, explainability, robustness, model drift, non-deterministic behaviour and Generative AI all require specialised testing knowledge and techniques.
The accredited ISTQB® Certified Tester AI Testing (CT-AI) training provides testing professionals with a solid foundation in testing AI-based systems. You will learn the fundamental concepts of Artificial Intelligence and Machine Learning, understand the quality characteristics specific to AI-based systems and apply testing techniques that address the unique challenges of machine learning models and Generative AI solutions.
Throughout the course you will gain insight into the complete AI system lifecycle. You will learn how training data is prepared, how machine learning models are developed and evaluated, which quality risks arise during development and deployment, and how these risks can be mitigated through effective testing. The course also covers Large Language Models (LLMs), prompt-based systems, red teaming, bias detection, model drift, adversarial testing and deployment-related testing.
The training follows the official ISTQB® Certified Tester AI Testing (CT-AI) v2.0 syllabus and combines theory with practical exercises. Participants will gain hands-on experience with machine learning workflows, data preparation, performance evaluation and AI-specific testing techniques. As a result, you will be well prepared for the certification exam and able to apply the acquired knowledge directly in real-world AI projects.
Special attention is given to the challenges of testing modern AI systems, including probabilistic behaviour, statistical testing approaches, the absence of traditional test oracles, and the growing importance of regulatory frameworks such as the EU AI Act. The course provides practical guidance on how testers can contribute to the quality, reliability and trustworthiness of AI-based systems throughout their lifecycle.
Target Audience
This course is intended for professionals involved in the development, implementation, validation or testing of AI-based systems.
The training is particularly suitable for:
- Software Testers
- Test Analysts
- Test Engineers
- Test Automation Engineers
- Test Consultants
- Test Managers
- Quality Assurance Professionals
- Software Developers
- Data Analysts
- Data Scientists
- Machine Learning Engineers
- Product Owners
- Business Analysts involved in AI initiatives
- Project Managers responsible for AI-based projects
The course is also valuable for professionals who want to understand the quality risks, testing challenges and validation approaches associated with Artificial Intelligence, Machine Learning and Generative AI systems.
Trainer: Johan van Berkel
