Exam Code: CT-GenAI
Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
Certification Provider: ISQI
Corresponding Certification: AI Testing

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Updated: Sep 12, 2026

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ISQI CT-GenAI Exam Syllabus Topics:

Section	Weight	Objectives
Fundamentals of Generative AI	20%	- Generative AI Concepts 1. Training data and context windows 2. Model types (Base, Instruction-tuned, RAG) 3. Alignment and guardrails 4. Emergent capabilities and limitations 5. Hallucinations 6. AI model behavior - AI Development Lifecycle 1. Model training and fine-tuning 2. Data collection, preparation, and curation 3. Evaluation 4. Deployment and monitoring - AI Terminology 1. Deep Learning 2. Large Language Models (LLMs) 3. Machine Learning (ML) 4. Artificial Intelligence (AI) 5. Tokens and prompts 6. Generative AI (GenAI) 7. Transformer architecture
Risks and Testing Challenges for Generative AI	30%	- Testing Challenges for Generative AI 1. Subjectivity of quality assessment 2. Coverage challenges 3. Non-deterministic output behavior 4. Complexity of the AI component 5. Ethical testing concerns 6. Regulatory and compliance considerations 7. Test oracle problem - Quality Risks Specific to Generative AI 1. Dependency on external components 2. Inappropriate output for the context 3. Input sensitivity (prompt brittleness) 4. Inconsistent responses across runs 5. Incorrect or fabricated outputs (hallucinations) 6. Offensive, harmful, or biased content
Tools for Testing Generative AI	20%	- Using Tools for Common Testing Activities 1. Prompt testing tools 2. Model evaluation tools 3. Simulation and monitoring tools 4. Security testing tools - Testing Tools Overview 1. Selecting appropriate tools for specific testing needs 2. Categories of GenAI testing tools
Testing Activities for Generative AI	30%	- Traceability and Documentation 1. Test coverage of AI model components 2. Documentation requirements for AI testing - Prompt-Based Testing 1. Test data creation with GenAI 2. Test case design using prompts 3. Prompt engineering basics - Requirements-Based Testing 1. Functional requirements for AI-based systems 2. Non-functional requirements for AI-based systems 3. AI-related quality requirements - Model and Output Evaluation 1. Output correctness and quality assessment 2. Checkpoint testing 3. Metamorphic testing 4. Human evaluation methods 5. Automated evaluation methods

ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:

Question #1

What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

A. Ability to trigger automated actions beyond conversation
B. Use of a conversational tone and improved response personalization
C. Reliance on predefined templates to generate short, factual answers
D. Ability to respond to prompts without explicit user instructions

Reveal Solution Discussion 0

Correct Answer: A 🗳️