ISTQB CT-AI real exam prep : Certified Tester AI Testing Exam

Exam Code: CT-AI
Exam Name: Certified Tester AI Testing Exam
Updated: Jun 23, 2026
Q&As: 162 Questions and Answers

ISTQB CT-AI Value Pack (Frequently Bought Together)

PDF Version: Convenient, easy to study. Printable ISTQB CT-AI PDF Format. It is an electronic file format regardless of the operating system platform.

PC Test Engine: Install on multiple computers for self-paced, at-your-convenience training.

Online Test Engine: Supports Windows / Mac / Android / iOS, etc., because it is the software based on WEB browser.

Value Pack Total: $179.97 $79.99

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 2	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 3	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 4	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 5	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 6	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 7	systems from those required for conventional systems.
Topic 8	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 9	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.