Announcement

 The CT-AI v1.0 certification will be retired: the English version will remain available until April 21, 2027, and non-English versions will remain available until October 21, 2027. After these dates, all exams and training courses based on the CT-AI v1.0 syllabus will no longer be available.  

Overview

The ISTQB® Certified Tester AI Testing (CT-AI) v2.0 certification focuses on testing AI-based systems, including machine learning systems and generative AI systems such as large language models. It provides the knowledge required to design and execute tests for AI-based systems, addressing their specific characteristics, including probabilistic behavior, non-determinism, and reliance on data. It also introduces AI-specific quality characteristics relevant to testing AI-based systems.

The syllabus follows a lifecycle-based approach, including input data testing, model testing, and ML development testing, together with relevant test approaches for modern AI-based systems.

Audience Exam Structure Business Outcomes More Exam Information Download Materials

Audience

The ISTQB® Certified Tester AI Testing (CT-AI) v2.0 certification is aimed at individuals involved in testing AI-based systems, including:

  • Testers, test analysts, and test engineers
  • Test managers
  • Test consultants
  • Data analysts and data scientists
  • Software developers involved in developing AI-based systems
  • User acceptance testers

It is also suitable for individuals seeking a general understanding of testing AI-based systems, such as:

  • Project managers
  • Quality managers
  • Software development managers
  • Business analysts
  • IT directors and management consultants

The ISTQB ® Certified Tester Foundation Level (CTFL) is a prerequisite for the CT-AI v2.0 certification.

Content

ISTQB® Certified Tester – AI Testing (CT-AI)

Introduction to Artificial Intelligence

Introduction to AI

Quality Characteristics for AI-Based Systems

Quality Characteristics for AI-Based Systems

Acceptance Criteria for AI-Based Systems

Machine Learning

Introduction to Machine Learning

Data for Machine Learning

ML Functional Performance Metrics for Classification

Neural Networks

Testing AI-Based Systems

Introduction to Testing AI-Based Systems

Testing Generative AI and Large Language Models

Test Levels and Machine Learning Systems

Input Data Testing for Machine Learning Systems

Input Data Testing for Machine Learning Systems

Model Testing for Machine Learning Systems

Model Testing for Machine Learning Systems

Machine Learning Development Testing

Machine Learning Development Testing

Exam Structure

  • No. of Questions: 40
  • Passing Score: 29
  • Total Points: 44
  • Exam Length (mins): 60 (+25% Non-Native Language)

Business Outcomes

Individuals who hold the ISTQB® Certified Tester- AI Testing certification should be able to accomplish the following business outcomes:

  • Understand the current state of AI, including generative AI. 
  • Experience the implementation and testing of machine learning models.
  • Understand the working and testing of simple neural networks.
  • Understand the specific AI quality characteristics defined by ISO/IEC 25059.
  • Calculate and interpret ML functional performance metrics for machine learning models.
  • Recognize the scope and importance of the two test levels that are specific to the testing of machine learning systems.
  • Contribute to the development of an effective test strategy for a machine learning system.
  • Design and execute test cases for machine learning systems.

More Information

Self-study, using the syllabus, sample exam, etc., is also an option when preparing for the exam.

Download Materials

Syllabus

Sample Exams

Exam Structures and Rules

General Files