ISTQB CT-AI (Certified Tester AI Testing): The Complete 2026 Guide

The ISTQB CT-AI (Certified Tester AI Testing) certification validates that a tester can systematically test AI-based systems — especially machine learning (ML) and generative AI such as large language models. The current syllabus, version 2.0, became available on 17 April 2026 and is the version every new candidate should study. The exam has 40 questions, a 65% pass mark, and requires CTFL as a prerequisite.

What is the ISTQB CT-AI certification?

CT-AI is a vendor-neutral specialist certificate in the ISTQB scheme. It does not teach you to build models; it teaches you to test them — to find where an AI system behaves incorrectly, unfairly, or unpredictably, and to judge its quality with the right metrics. It sits above the Foundation Level (CTFL) and targets testers, test analysts, test engineers, and the data scientists and analysts who want to understand how their data and models will be validated.

What is new in CT-AI v2.0 (2026)?

Version 2.0 is a substantial rewrite, not a refresh. The headline changes are:

• Reorganised from 11 chapters down to 7, restructured around data, the ML model, and the system as a whole.

• Generative AI and LLM testing added: prompt robustness, hallucination and groundedness, non-determinism, and red teaming.

• Two explicit ML test levels: input-data testing (representativeness, label quality, leakage) and model testing (metrics, metamorphic and back-to-back testing).

• Deployment testing, including data drift and rollout strategies such as canary releases.

• Modern model practice: pretrained models, fine-tuning, and retrieval-augmented generation (RAG).

• The "using AI for testing" block was removed and now lives in the separate CT-GenAI certification.

Recommended training time also dropped from 26 hours (v1.0) to 19.5 hours, but the material is denser and more hands-on. Our dedicated CT-AI v2.0 vs v1.0 comparison covers every change in detail.

Exam format: questions, time, pass mark

The CT-AI exam is consistent with other ISTQB specialist exams:

• 40 multiple-choice questions, scored on knowledge levels K1–K3.

• 65% to pass — that is 26 of 40 points (some questions are worth more than one point).

• 60 minutes, extended to 75 minutes if you do not sit the exam in your native language.

• CTFL is required before you can certify.

What the CT-AI v2.0 syllabus covers

Quality characteristics of AI systems

AI systems introduce quality factors that classic software does not have: bias and fairness, transparency and explainability, autonomy, adaptability, and non-determinism. A correct-looking output can still be wrong if the model is biased or the result cannot be explained. CT-AI teaches you to treat these as first-class test objectives.

Testing machine learning systems

This is the core of the exam. You test the data before the model (completeness, representativeness, label correctness, leakage) and then the model itself using metrics such as precision, recall, and F1 read from a confusion matrix. Techniques include metamorphic testing, back-to-back testing against a reference implementation, and A/B testing in production. Understanding why accuracy alone is misleading on imbalanced data is a frequent exam theme.

Testing generative AI and LLMs

The newest material. Because LLM output is non-deterministic, you cannot rely on a single expected result; instead you test for groundedness, factuality, safety, and robustness against adversarial prompts. Red teaming — deliberately probing a model for unsafe or policy-breaking behaviour — is now part of the syllabus, alongside RAG-specific risks and evaluation metrics.

Prerequisites and who should take it

The only hard prerequisite is CTFL. Beyond that, CT-AI suits testers moving into AI projects, automation engineers validating ML pipelines, and data scientists who want a shared vocabulary with QA. You do not need to be able to train a model from scratch, but comfort with basic statistics and the idea of a confusion matrix will help.

How to prepare

Start from the official CT-AI syllabus and the ISTQB glossary, then drill the harder K3 areas — metric calculation, input-data defects, and LLM evaluation — with realistic practice questions rather than rote definitions. Our free CT-AI mock exam mirrors the real format (40 questions, 65% pass) and includes scenario and confusion-matrix items with a written rationale on every answer, so you learn why an option is right or wrong. For a week-by-week plan, a dedicated How to Pass the ISTQB CT-AI Exam study guide is on the way.

If you have not yet earned CTFL, start with our complete ISTQB CTFL Foundation guide — it is the gateway to CT-AI and to the wider ISTQB path.