ISTQB AI Testing Sample exams 2026

This course is preparation for CT-AI exam, it will help you go through all the sections of syllabus with sample questions
You can verify your knowledge for every type of content provided to pass exam.
Course is sorted like syllabus sections:

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)

• 1.8 Pre-Trained Models

• 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

• 3.2 ML Workflow

• 3.3 Selecting a Form of ML

• 3.4 Factors Involved in ML Algorithm Selection

• 3.5 Overfitting and Underfitting

4. ML - Data

• 4.1 Data Preparation as Part of the ML Workflow

• 4.2 Training, Validation and Test Datasets in the ML Workflow

• 4.3 Dataset Quality Issues

• and other