ISTQB Testing with Generative AI Sample Exams 2025

This course is preparation for CT-GenAI exam, it will help you go through all the sections of syllabus with sample questions. Course is sorted like syllabus sections:1 Introduction to Generative AI for Software Testing1.1 Generative AI Foundations and Key Concepts1.1.1 AI Spectrum: Symbolic AI, Classical Machine Learning, Deep Learning, and Generative AI1.1.2 Basics of Generative AI and LLMs1.1.3 Foundation, Instruction-Tuned and Reasoning LLMs1.1.4 Multimodal LLMs and Vision-Language Models1.2 Leveraging Generative AI in Software Testing: Core Principles1.2.1 Key LLM Capabilities for Test Tasks1.2.2 AI Chatbots and LLM-Powered Testing Applications for Software Testing2 Prompt Engineering for Effective Software Testing2.1 Effective Prompt Development2.1.1 Structure of Prompts for Generative AI in Software Testing2.1.2 Core Prompting Techniques for Software Testing2.1.3 System Prompt and User Prompt2.2 Applying Prompt Engineering Techniques to Software Test Tasks2.2.1 Test Analysis with Generative AI2.2.2 Test Design and Test Implementation with Generative AI2.2.3 Automated Regression Testing with Generative AI2.2.4 Test Monitoring and Test Control with Generative AI2.2.5 Choosing Prompting Techniques for Software Testing2.3 Evaluate Generative AI Results and Refine Prompts for Software Test Tasks2.3.1 Metrics for Evaluating the Results of Generative AI on Test Tasks2.3.2 Techniques for Evaluating and Iteratively Refining Prompts3 Managing Risks of Generative AI in Software Testing3.1 Hallucinations, Reasoning Errors and Biases3.1.1 Hallucinations, Reasoning Errors and Biases in Generative AI3.1.2 Identify Hallucinations, Reasoning Errors and Biases in LLM Output3.1.3 Mitigation Techniques of GenAI Hallucinations, Reasoning Errors and Biases in Software Test Tasks3.1.4 Mitigation of Non-Deterministic Behavior of LLMs3.2 Data Privacy a