What you'll learn:
- Understand AI fundamentals and AI-based systems
- Identify key quality aspects like bias and ethics
- Learn core machine learning concepts and workflows
- Understand data preparation and dataset types
- Use ML metrics like confusion matrix
- Learn how to test AI-based systems
- Handle challenges like bias and concept drift
- Explore AI testing techniques and methods
- Use AI to support testing activities
- Prepare for the ISTQB CT-AI exam
This course contains the use of artificial intelligence.
Welcome to the "Exam Preparation: ISTQB CT-AI" Course!
This course is your complete guide to understanding and mastering the concepts required to successfully pass the ISTQB Certified Tester – AI Testing (CT-AI) exam.
It has been carefully designed based on the official syllabus, covering all key topics step by step — from AI fundamentals, through machine learning concepts, to practical approaches for testing AI-based systems.
Whether you're a software tester looking to expand your skills into AI, or preparing specifically for the CT-AI certification, this course will help you build both confidence and real understanding of the subject.
What You’ll Learn:
AI Fundamentals & Concepts
Understand what artificial intelligence really is and how it differs from traditional systems. Learn about narrow, general, and super AI, as well as key AI technologies and development approaches.
Quality Characteristics of AI-Based Systems
Explore critical aspects such as autonomy, adaptability, bias, ethics, transparency, and safety. Learn why quality in AI systems is more complex than in conventional software.
Machine Learning Essentials
Get a clear understanding of machine learning workflows, different types of ML, and how models are built and evaluated. Learn about overfitting, underfitting, and algorithm selection.
Data in Machine Learning
Understand the importance of data preparation, dataset quality, and labeling. Learn how training, validation, and test datasets impact model performance.
ML Performance Metrics
Master key metrics such as the confusion matrix and evaluation techniques for classification, regression, and clustering. Learn their limitations and how to choose the right ones.
Neural Networks & Testing
Learn the basics of neural networks and how they are tested. Explore coverage measures and challenges related to testing complex AI models.
Testing AI-Based Systems
Understand how to approach testing in AI systems, including test levels, test data, and challenges such as concept drift and non-deterministic behavior.
AI-Specific Testing Challenges
Dive into topics like bias, probabilistic behavior, explainability, and autonomous systems. Learn how these challenges affect testing strategies and outcomes.
Testing Methods & Techniques
Explore modern testing techniques such as A/B testing, metamorphic testing, adversarial attacks, and data poisoning — and understand when to use them.
Test Environments for AI
Learn how to design and use environments for testing AI systems, including virtual test environments.
Using AI for Testing
Discover how AI can support testers through test case generation, defect prediction, regression optimization, and more.
Practical Exam Preparation
Validate your knowledge with a sample exam designed to simulate real CT-AI exam conditions and help you assess your readiness.
Who This Course Is For:
Aspiring CT-AI Candidates
Anyone preparing for the ISTQB CT-AI exam who wants a structured and complete learning path aligned with the official syllabus.
QA Professionals and Testers
Testers who want to expand their knowledge into AI-based systems and stay competitive in the evolving IT landscape.
Software & AI Practitioners
Developers, analysts, and engineers who want to understand how AI systems are tested and what makes them different from traditional software.
You will have a solid understanding of AI testing concepts, machine learning fundamentals, and practical testing approaches required for AI-based systems.
More importantly, you will be well-prepared to pass the ISTQB CT-AI certification exam and confidently apply this knowledge in real-world scenarios.
