ISTQB CT-AI (Certified Tester AI Testing)
ISTQB® CT-AI certifies testing of AI-based systems (ML + generative AI/LLMs). Syllabus v2.0 is current; v1.0 English exams run until 21 Apr 2027 (non-English until 21 Oct 2027). Exam: 40 MCQs, 60 min, 44 points (pass 29 ≈ 66%). Higher K-level questions score >1 point; non-native speakers get +25% time. Prerequisite: CTFL. It covers the real challenges of AI testing—probabilistic behaviour, non-determinism, data dependence—across seven areas including quality characteristics (ISO/IEC 25059), input data testing, model testing and ML development testing. Aimed at testers, test managers, data scientists and developers. Delivered year-round by accredited providers (fee set locally, e.g. Indian Testing Board in India). Do not confuse with CT-GenAI (which is about *using* GenAI for testing).
Quizzes
10 questions · ~10 minutes · instant rank & AI diagnosis
Dual AI-verified questions Real exam pattern 2 quizzes free, then 10 credits
ISTQB CT-AI — Model Testing II and ML Development Testing: Drift, Overfitting, A/B, Back-to-Back and Deployment
ISTQB CT-AI — Model Testing I: Model Risks, Documentation Review, Statistical Performance Testing and Metamorphic Testing
ISTQB CT-AI — Input Data Testing: Bias, Data Pipelines, Representativeness, Constraints and Labels
ISTQB CT-AI — Testing AI-Based Systems II: Testing Generative AI and Red Teaming
ISTQB CT-AI — Testing AI-Based Systems I: Locked and Adaptive Systems, Test Oracles and Test Levels
ISTQB CT-AI — Machine Learning II: Functional Performance Metrics, Neural Networks and Coverage
ISTQB CT-AI — Machine Learning I: Forms of ML, the ML Workflow, Fine-Tuning and Datasets
ISTQB CT-AI — Quality Characteristics for AI-Based Systems: ISO/IEC 25059, Safety and Acceptance Criteria
ISTQB CT-AI — Introduction to AI II: Hardware, Hosting, ML Frameworks and AI Regulation
ISTQB CT-AI — Introduction to AI I: AI-Based Systems, Narrow to Super AI, and Generative AI