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ISQI CT-GenAI Exam Syllabus Topics:

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

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

Question #1

Which AI approach requires feature engineering and structured data preparation?

A. Classical Machine Learning
B. Symbolic AI
C. Generative AI
D. Deep Learning

Answer: A