Exam Preparation: Istqb Ct-Ai
 

Prepare for ISTQB CT-AI exam: AI testing, machine learning, metrics, techniques & practice questions
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
Requirements
Basic understanding of software testing concepts
Familiarity with IT fundamentals
Willingness to learn and prepare for the CT-AI exam
Description
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
Testers preparing for the ISTQB CT-AI exam
QA professionals interested in AI testing
Software testers expanding into AI and ML topics