ISTQB Certified Tester AI Testing (CT-AI) - Instructor-Led Live Training
Course code:
ISTQB Certified Tester AI Testing (CT-AI) - Instructor-Led Live Training - Dates and application
First training day: 26 October 2026 , Additional training days: 27., 28.
Please submit your enrolment and we shall endeavour to secure a place for you.
First training day: 30 November 2026 , Additional training days: 1 December , 2.
First training day: 22 February 2027 , Additional training days: 23., 24.
First training day: 28 April 2027 , Additional training days: 29., 30.
Applying for closed-group training
Enrolment without a date
Discover the world of testing AI-based systems with the ISTQB Certified Tester AI Testing preparation training program! This professional development program equips you with the skills to test AI systems and machine learning models, tackle challenges like biases, ethical considerations, and transparency, and master practical techniques to enhance your expertise. The training program is designed to help you apply your knowledge in production environment projects and prepare confidently for the international certification.
This program is ideal for software testers, test analysts, and software engineers involved in AI-based systems. It’s also perfect for project managers, quality managers, and business analysts seeking a foundational understanding of AI testing techniques and challenges. If you want to ensure high-quality AI systems while staying ahead in the evolving field of software testing, this training is for you.
- ISTQB Certified Tester Foundation Level certification
- English reading skills, given that both the training materials and the exam are in English.
- Knowledge of the theoretical background of artificial intelligence is NOT a prerequisite for the training.
This comprehensive program is designed for professionals involved or plan to be involved in testing AI-based systems. Our accredited training material, built on the official ISTQB syllabus, provides comprehensive preparation for the CT-AI certification exam. The professional development program offers both theoretical and practical knowledge, enabling participants to effectively test, optimize, and ensure the quality of AI-based systems and processes. Our training places a strong emphasis on the theoretical background of artificial intelligence, which is also thoroughly tested in the related ISTQB exam.
Main topics:
- Introduction to AI: Learn the fundamental concepts of AI, its types (narrow, general, and super AI), technologies, development frameworks, and the possibilities of AI as a service.
- Quality Characteristics for AI-Based Systems: Discover key quality factors of AI systems, including adaptability, transparency, robustness, ethics, and safety, as well as AI-specific acceptance criteria.
- Machine Learning Overview: Gain an understanding of machine learning types, workflows, algorithm selection, the importance and management of data, data splitting, functional performance metrics, neural networks, and coverage metrics.
- Testing AI-Based Systems: Understand the key challenges and characteristics of testing AI-based and Generative AI-based systems, including new testing levels such as data and unit testing, as well as AI-specific activities at traditional testing levels.
- Input Data Testing: Explore the importance of input data, related risks, and testing methods for mitigating these risks, including bias, data pipeline, representativeness, constraints, and label correctness testing.
- Machine Learning Model Testing: Learn about model-related risks and testing methods for mitigating them, including statistical, adversarial, metamorphic, drift, A/B, and back-to-back testing, as well as the concepts of underfitting and overfitting.
- Testing Machine Learning Method Development: Understand the specific risks arising during the development of ML-based systems and how deployability, rollback, canary, shadow, model conversion, cross-device, and API testing can help mitigate these risks.
By completing the ISTQB® Certified Tester- AI Testing qualification, course participants will:
- understand the current state of artificial intelligence and expected trends,
- gain experience in the implementation of machine learning models,
- become familiar with the challenges of tests completed in intelligent systems,
- gain experience in designing and implementing test cases in intelligent systems,
- recognise the specific requirements for testing in intelligent systems.
CURRICULUM OUTLINE:
1 Introduction to Artificial Intelligence
1.1 AI-Based and Conventional Systems
1.2 Narrow AI, General AI, and Super AI-Based
1.3 Different Types of AI Technologies
1.4 Generative AI
1.5 Hardware for Machine Learning Systems
1.6 Development and Hosting of AI Models
1.7 Machine Learning Development Frameworks
1.8 Regulations and Standards for AI
2 Quality Characteristics for AI-Based Systems
2.1 Quality Characteristics for AI-Based Systems
2.1.1 AI-Specific Quality Characteristics
2.1.2 AI and Safety
2.2 Acceptance Criteria for AI-Based Systems
2.2.1 Acceptance Criteria for AI-Based Systems
3 Machine Learning
3.1 Introduction to Machine Learning
3.1.1 Different Forms of Machine Learning
3.1.2 Machine Learning Workflow
3.1.3 Hands-on Exercise: Create a Machine Learning Model
3.1.4 Pretrained Models, Fine-Tuning, and Retrieval-Augmented Generation
3.2 Data for Machine Learning
3.2.1 Activities in Data Preparation
3.2.2 Hands-on Exercise: Data Preparation in Support of the Creation of a Machine Learning Model
3.3 ML Functional Performance Metrics for Classification
3.3.1 Calculation of Machine Learning Functional Performance Metrics
3.3.2 Hands-on Exercise: Evaluate a Machine Learning Model using Selected ML Functional Performance Metrics
3.3.3 Hands-on Exercise: Show the Impact of Different Machine Learning Models and Dataset Combinations
3.4 Neural Networks
3.4.1 Structure and Working of a Deep Neural Network
3.4.2 Hands-on Exercise: Experience the Implementation of a Perceptron
3.4.3 Coverage Measures for Neural Networks
4 Testing AI-Based Systems
4.1 Introduction to Testing AI-Based Systems
4.1.1 Locked and Adaptive AI-Based Systems
4.1.2 Rationale for a Statistical Approach to Testing AI-Based Systems
4.1.3 Test Oracles for AI-Based Systems
4.2 Testing Generative AI and Large Language Models
4.2.1 Testing Generative AI 41 4.2.2 Red Teaming
4.2.3 Hands-on Exercise: Exploratory Testing of a Large Language Model
4.3 Test Levels and Machine Learning Systems
4.3.1 Test Levels for Machine Learning Systems
4.3.2 Risk-Based Testing of Machine Learning Systems
5 Input Data Testing for Machine Learning Systems
5.1 Input Data Risks and Mitigations
5.2 Testing for Bias
5.3 Data Pipeline Testing
5.4 Testing for Data Representativeness
5.5 Dataset Constraint Testing
5.6 Label Correctness Testing
5.7 Hands-on Exercise: Input Data Testing
6 Model Testing for Machine Learning Systems
6.1 Machine Learning Model Risks and Mitigations
6.2 Machine Learning Model Documentation and Review
6.3 ML Functional Performance Testing of Probabilistic Machine Learning Systems
6.4 Adversarial Testing of Machine Learning Systems
6.5 Metamorphic Testing
6.6 Hands-on Exercise: Apply Metamorphic Testing
6.7 Drift Testing
6.8 Testing for Overfitting and Underfitting
6.9 A/B Testing
6.10 Back-to-Back Testing
7 Machine Learning Development Testing
7.1 Machine Learning Development Risks and Mitigations
7.2 Machine Learning System Deployment Testing
Instructors
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Course Administrator
I was satisfied with everything! The curriculum is excellently structured, the examples are understandable, and Attila is a great instructor. Thanks to him and all the staff at Masterfield!
Gábor Kövesi
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