ISTQB Certified Tester AI (Artificial Intelligence) Testing live course
Course code:
Course length:
Training language:
32 lessons
English
Course fee:
349 500 HUF + VAT (905 EUR + VAT)
ISTQB Certified Tester AI (Artificial Intelligence) Testing live course - Dates and application
First training day: 24 November 2025, Further training days: 25., 26., 27.
Weekday daytime (09.00 - 16.30)
Application deadline:
12 November 2025
Training language:
English
Course fee:
349 500 HUF + VAT (905 EUR + VAT)
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Discover the world of testing AI-based systems with the ISTQB Certified Tester AI Testing preparation course! This training 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 course is designed to help you apply your knowledge in real-world projects and prepare confidently for the international certification.
This course is ideal for software testers, test analysts, and software engineers involved in AI-based systems or using AI in testing. 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 course material and the exam are in English.
- Knowledge of the theoretical background of artificial intelligence is NOT a prerequisite for the training.
This course is designed for professionals involved or plan to be involved in testing AI-based systems or using AI tools for testing purposes. Our accredited training material, built on the official ISTQB syllabus, provides comprehensive preparation for the CT-AI certification exam. The training 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 flexibility, ethics, biases, transparency, and safety.
- Machine Learning Overview: Gain an understanding of machine learning types, workflows, algorithm selection, and handling issues like overfitting and underfitting.
- ML Data Preparation and Management: Master data preparation steps, the importance of dataset quality, and the role of annotation in machine learning models.
- ML Functional Performance Metrics: Learn to apply and evaluate performance metrics for ML models in classification, regression, and clustering tasks.
- Neural Networks and Their Testing: Dive into the workings and testing of neural networks, including coverage metrics and implementing simple perceptions.
- Testing AI-Based Systems Overview: Understand the testing levels for AI systems, including data testing, component and system integration testing, and acceptance testing.
- Testing AI-Specific Quality Characteristics: Explore how to test AI systems for autonomy, self-learning capabilities, biases, and transparency.
- Methods and Techniques for Testing AI-Based Systems: Master AI-specific testing techniques, such as adversarial attacks, data poisoning, and metamorphic testing.
- Test Environments for AI-Based Systems: Learn the importance of virtual test environments in validating AI-based systems and testing operational models.
- Using AI for Testing: Discover how AI tools can be used for test case generation, defect prediction, regression test suite optimization, and user interface testing.
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.
COURSE OUTLINE:
1. INTRODUCTION TO AI
1.1 Definition of AI and AI Effect
1.2 Narrow, General and Super AI
1.3 AI-Based and Conventional Systems
1.4 AI Technologies
1.5 AI Development Frameworks
1.6 Hardware for AI-Based Systems
1.7 AI as a Service (AIaaS)
1.7.1 Contracts for AI as a Service
1.7.2 AIaaS Examples
1.8 Pre-Trained Models
1.8.1 Introduction to Pre-Trained Models
1.8.2 Transfer Learning
1.8.3 Risks of using Pre-Trained Models and Transfer Learning
1.9 Standards, Regulations and AI
2. QUALITY CHARACTERISTICS FOR AI-BASED SYSTEMS
2.1 Flexibility and Adaptability
2.2 Autonomy
2.3 Evolution
2.4 Bias
2.5 Ethics
2.6 Side Effects and Reward Hacking
2.7 Transparency, Interpretability and Explainability
2.8 Safety and AI
3. MACHINE LEARNING (ML) – OVERVIEW
3.1 Forms of ML
3.1.1 Supervised Learning
3.1.2 Unsupervised Learning
3.1.3 Reinforcement Learning
3.2 ML Workflow
3.3 Selecting a Form of ML
3.4 Factors Involved in ML Algorithm Selection
3.5 Overfitting and Underfitting
3.5.1 Overfitting
3.5.2 Underfitting
3.5.3 Hands-On Exercise: Demonstrate Overfitting and Underfitting
4. ML - DATA
4.1 Data Preparation as Part of the ML Workflow
4.1.1 Challenges in Data Preparation
4.1.2 Hands-On Exercise: Data Preparation for ML
4.2 Training, Validation and Test Datasets in the ML Workflow
4.2.1 Hands-On Exercise: Identify Training and Test Data and Create an ML Model
4.3 Dataset Quality Issues
4.4 Data Quality and its Effect on the ML Model
4.5 Data Labelling for Supervised Learning
4.5.1 Approaches to Data Labelling
4.5.2 Mislabeled Data in Datasets
5. ML FUNCTIONAL PERFORMANCE METRICS
5.1 Confusion Matrix
5.2 Additional ML Functional Performance Metrics for Classification, Regression and
Clustering
5.3 Limitations of ML Functional Performance Metrics
5.4 Selecting ML Functional Performance Metrics
5.4.1 Hands-On Exercise: Evaluate the Created ML Model
5.5 Benchmark Suites for ML
6. ML - NEURAL NETWORKS AND TESTING
6.1 Neural Networks
6.1.1 Hands-On Exercise: Implement a Simple Perceptron
6.2 Coverage Measures for Neural Networks
7. TESTING AI-BASED SYSTEMS OVERVIEW
7.1 Specification of AI-Based Systems
7.2 Test Levels for AI-Based Systems
7.2.1 Input Data Testing
7.2.2 ML Model Testing
7.2.3 Component Testing
7.2.4 Component Integration Testing
7.2.5 System Testing
7.2.6 Acceptance Testing
7.3 Test Data for Testing AI-based Systems
7.4 Testing for Automation Bias in AI-Based Systems
7.5 Documenting an AI Component
7.6 Testing for Concept Drift
7.7 Selecting a Test Approach for an ML System
8. TESTING AI-SPECIFIC QUALITY CHARACTERISTICS
8.1 Challenges Testing Self-Learning Systems
8.2 Testing Autonomous AI-Based Systems
8.3 Testing for Algorithmic, Sample and Inappropriate Bias
8.4 Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems
8.5 Challenges Testing Complex AI-Based Systems
8.6 Testing the Transparency, Interpretability and Explainability of AI-Based Systems
8.6.1 Hands-On Exercise: Model Explainability
8.7 Test Oracles for AI-Based Systems
8.8 Test Objectives and Acceptance Criteria
9. METHODS AND TECHNIQUES FOR THE TESTING OF AI-BASED SYSTEMS
9.1 Adversarial Attacks and Data Poisoning
9.1.1 Adversarial Attacks
9.1.2 Data Poisoning
9.2 Pairwise Testing
9.2.1 Hands-On Exercise: Pairwise Testing
9.3 Back-to-Back Testing
9.4 A/B Testing
9.5 Metamorphic Testing (MT)
9.5.1 Hands-On Exercise: Metamorphic Testing
9.6 Experience-Based Testing of AI-Based Systems
9.6.1 Hands-On Exercise: Exploratory Testing and Exploratory Data Analysis (EDA)
9.7 Selecting Test Techniques for AI-Based Systems
10. TEST ENVIRONMENTS FOR AI-BASED SYSTEMS
10.1 Test Environments for AI-Based Systems
10.2 Virtual Test Environments for Testing AI-Based Systems
11. USING AI FOR TESTING
11.1 AI Technologies for Testing
11.1.1 Hands-On Exercise:The Use of AI in Testing
11.2 Using AI to Analyze Reported Defects
11.3 Using AI for Test Case Generation
11.4 Using AI for the Optimization of Regression Test Suites
11.5 Using AI for Defect Prediction
11.5.1 Hands-On Exercise: Build a Defect Prediction System
11.6 Using AI for Testing User Interfaces
11.6.1 Using AI to Test Through the Graphical User Interface (GUI)
11.6.2 Using AI to Test the GUI
Trainers
He is an adjunct lecturer at the Department of Software Development at the University of Szeged. In addition to his teaching work, he is also active in research, with numerous studies on software development and testing published in English. His main competency areas include information security, programming, requirement management, and software testing.
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Course administrator
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Lőte Petra
Jacobs Douwe Egberts Zrt.