Certified Tester AI Testing (CT-AI) Version 2.0
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.
Note: The CT-AI v2.0 certification replaces CT-AI v1.0. Candidates interested in using generative AI to support testing activities should consider the ISTQB® Certified Tester Testing with Generative AI (CT-GenAI) certification, which focuses on the application of generative AI in the testing process.
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: 47
- 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
We highly recommend attending accredited training as it ensures that an ISTQB® Member Board has assessed the materials for relevance and consistency against the syllabus. Search for an Accredited Training Provider here.
Self-study, using the syllabus, sample exam, etc., is also an option when preparing for the exam.
Exams and certification for CT-AI v2.0 are available from ISTQB® exam providers. You can find an exam provider for CT-AI v2.0 here.
Holders of this certification may choose to proceed to other Core, Agile, or Specialist stream certifications.
Find out more on our FAQ page for CT-AI v2.0 .
