ISTQB CT-AI
ISTQB® Certified Tester AI (CT-AI)
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ExploreAbout this exam
ISTQB® Certified Tester AI (CT-AI) is a specialist certification for professionals who test AI-based systems. Version 2.0 restructured the syllabus from 11 chapters down to 7 and reorganized it around the machine learning lifecycle - input data testing, model testing and ML development testing - while narrowing the scope exclusively to testing AI-based systems. Content on using AI to perform testing has been removed and now lives in the separate CT-GenAI certification. The exam therefore spans AI and machine learning fundamentals, AI quality characteristics aligned with ISO/IEC 25059, ML workflows, input data quality, model evaluation, ML development testing, and dedicated coverage of generative AI and large language models, including exploratory testing and red teaming. The qualification targets testers, test analysts, test engineers, developers, data professionals and test managers, and assumes no prior AI expertise beyond the CTFL prerequisite. The exam consists of 40 multiple-choice questions in 60 minutes, with a 65% pass mark and 25% additional time for candidates sitting it in a non-native language. Preparation should center on the official Version 2.0 syllabus and its hands-on exercises covering ML workflows, data preparation and model evaluation; accredited training has been reduced to three days.
What you'll study
About ISTQB CT-AI preparation
What is the exam format for CT-AI Version 2.0?
The format is 40 multiple-choice questions with a 60-minute duration and a pass mark of 65%. Candidates taking the exam in a non-native language receive 25% additional time. The exam is based on the Version 2.0 syllabus and its learning objectives, so study materials must match that version.
Is the CTFL certification still a prerequisite?
Yes. The ISTQB® Certified Tester Foundation Level (CTFL) certification remains the entry requirement for taking the CT-AI exam. You should hold CTFL before booking, since CT-AI builds on Foundation Level terminology and assumes familiarity with software testing or development practices.
Do I need prior AI or machine learning knowledge?
No. No prior AI expertise is required beyond the Foundation Level prerequisite. The syllabus introduces key artificial intelligence and machine learning concepts before addressing testing approaches, so candidates with a testing background but no data science experience can follow the material.
Should I prepare for Version 1.0 or switch to Version 2.0?
It depends on your timeline. If your Version 1.0 preparation is well advanced and you will sit the exam within the transition period, continuing may be more efficient. Version 2.0 is the more focused framework if your goal is skills specifically in testing AI-based systems.
How long is Version 1.0 still available?
Version 1.0 remains available for English-language training and examinations, including retakes, until 21st April 2027, and for other languages until 21st October 2027. After those dates all training and examinations are based on Version 2.0.
My certificate is from Version 1.0 - is it still valid?
Yes. Certifications obtained under Version 1.0 remain valid, and there is no mandatory requirement to retake the exam or upgrade. Note there is no bridging exam: Version 2.0 must be taken as a separate certification exam if you want the newer qualification.
Does Version 2.0 cover using AI to do testing?
No. Content related to using AI for testing has been removed from Version 2.0, which focuses exclusively on testing AI-based systems. For using large language models and generative AI across the test process, ISTQB® offers the separate CT-GenAI certification.
Does the syllabus align with any standards?
Yes. Version 2.0 aligns with emerging standards for AI quality characteristics, including ISO/IEC 25059, reflecting a more structured approach to defining and assessing quality in AI-based systems. Expect questions on quality characteristics framed around that standard's view of AI system quality.
Does the training include practical exercises?
Yes. Version 2.0 includes hands-on exercises across key areas including machine learning workflows, data preparation, model evaluation and testing approaches, supporting practical application of syllabus concepts. Accredited classroom training has been reduced to three days, and training providers must reaccredit their materials for Version 2.0.
What is a common way candidates lose marks?
Candidates frequently under-prepare the data and model areas that Version 2.0 emphasizes - input data testing, model testing and ML development testing - treating them as background rather than exam targets. Work through the syllabus learning objectives in order, then use hands-on data preparation and model evaluation exercises to make the terminology concrete.