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ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. Who typically defines the system prompt in a testing workflow?

A) Product owner in user stories only
B) End user during normal chat use
C) CI server automatically without human input
D) A tester configuring the assistant

2. What is a key data-related aspect when defining a GenAI strategy for testing?

A) Aggregate data from all available organizational repositories without filtration
B) Neglect legacy data sources as they provide limited immediate relevance to testing tasks
C) Prioritize accurate and relevant input data secured through defined quality procedures
D) Use only auto-generated synthetic data to avoid dependency on enterprise repositories

3. You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

A) Detect requirement defects -> generate conditions -> prioritize
B) Generate prioritized conditions in one shot -> verify coverage
C) Generate conditions -> prioritize by risk -> map to requirements to find gaps
D) Prioritize requirements -> generate conditions -> review defects

4. How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

A) Moving from black-box exploratory testing toward exclusively performing code-based white-box checks
B) Replacing existing test coverage validation with automated summary reports generated by AI
C) Shifting from test execution toward reviewing, refining, and validating AI-generated testware
D) Transitioning from manual execution to complete automation with no human oversight

5. An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?

A) Hallucination
B) Dataset bias toward UI features
C) Reasoning error in risk calculation logic
D) No defect; this is acceptable

Solutions:

Question # 1
Answer: D

Question # 2
Answer: C

Question # 3
Answer: C

Question # 4
Answer: C

Question # 5
Answer: C