Version 2.0 introduces a more focused and structured approach to testing AI-based systems. The syllabus has been reduced from 11 chapters to 7 and reorganized around the machine learning lifecycle, including input data testing, model testing, and ML development testing.

The scope now focuses exclusively on testing AI-based systems. Content related to using AI for testing has been removed. For those interested in this area, ISTQB® offers the CT-GenAI certification.

Version 2.0 CT-AI introduces dedicated coverage of testing generative AI and large language models, including techniques such as exploratory testing and red teaming.

There is also a strong emphasis on practical skills, with hands-on exercises covering machine learningworkflows, data preparation, performance evaluation, and testing techniques. In addition, the syllabusaligns more closely with emerging standards for AI quality characteristics.

Not necessarily. If you are already well advanced in your preparation for Version 1.0 and plan to take the exam within the transition period, it may be more efficient to continue.

However, if your goal is to develop skills specifically in testing AI-based systems, Version 2.0 provides amore focused and up-to-date framework. It places greater emphasis on areas such as input data testing,model testing, and ML development testing.Version 1.0 also included content on using AI for testing, which is no longer part of Version 2.0.If your interest lies in that area, you may prefer to complete Version 1.0 or explore other ISTQB®certifications, such as CT-GenAI certification.

Version 1.0 will remain available for English-language training and examinations (including retakes) until 21st April 2027 and for other languages until 21st October 2027.

After these periods, all training and examinations will be based on Version 2.0.

Yes. Training providers must update their course materials and undergo reaccreditation to align withVersion 2.0.

This is a major update to the syllabus, with significant changes in both structure and content. It has beenstreamlined from 11 chapters to 7 (reduced to 3 days of training) and refocused exclusively on testing ofAI-based systems, and reorganized around areas such as input data testing, model testing, and MLdevelopment testing. Version 2.0 also adds dedicated coverage of generative AI and large languagemodels within that broader AI testing context, while removing content related to using AI for testing.

Reaccreditation is therefore necessary to ensure that training remains accurate, relevant, and alignedwith the current syllabus.

Yes. Certifications obtained under Version 1.0 remain valid. There is no mandatory requirement to retake the exam or upgrade your certification.

Yes. The ISTQB® Certified Tester Foundation Level (CTFL) certification remains the entry requirementfor taking the CT-AI exam.

The exam for Version 2.0 will be based on the Version 2.0 syllabus and its learning objectives.

The exam format remains unchanged:

• 40 multiple-choice questions
• 60-minute duration
• 25% additional time for candidates taking the exam in a non-native language
• Pass mark of 65%

No. There is no bridging exam. Version 1.0 certificates remain valid, and Version 2.0 needs to be takenas a separate certification exam.

No prior AI expertise is required beyond the Foundation Level prerequisite. The syllabus introduces keyconcepts in artificial intelligence and machine learning before addressing testing approaches.

Some familiarity with software testing or development practices is recommended.

Yes. Version 2.0 includes hands-on exercises across key areas, including machine learning workflows, data preparation, model evaluation, and testing approaches. These exercises support the practical application of the concepts defined in the syllabus.

Version 2.0 aligns with emerging standards for AI quality characteristics, including ISO/IEC 25059. Thisreflects a more structured approach to defining and assessing quality in AI-based systems.

Version 2.0 is intended for professionals involved in testing AI-based systems, including testers, testanalysts, test engineers, developers, data professionals, and test managers. It is also suitable foranyone seeking a structured understanding of AI testing concepts and practices.