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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ISQI CT-GenAI Exam Syllabus Topics:

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

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

Question #1

Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

A. It dynamically generates test insights using contextual information
B. It focuses primarily on visual dashboards and user navigation features
C. It produces scripted conversational responses similar to traditional bots
D. It provides fixed responses from predefined rule sets and scripts

Answer: A