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ISQI CT-GenAI Exam Syllabus Topics:

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

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

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

2. What are the three key phases in adopting GenAI in a test organization?

A) Prototype; pilot; decommission
B) Discovery; initiation and usage definition; utilization and iteration
C) Planning; execution; sign-off
D) Training; certification; outsourcing

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

4. Which statement about fine-tuning for test tasks is INCORRECT?

A) It replaces the model's general knowledge entirely and prevents overfitting
B) It enhances relevance to organizational terminology and formats
C) It can be applied to smaller SLMs to improve task performance with lower compute
D) It adapts a pre-trained model to a domain using task-specific data

5. A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

A) Data poisoning
B) Request manipulation
C) Data exfiltration
D) Malicious code generation

Solutions:

Question # 1
Answer: B

Question # 2
Answer: B

Question # 3
Answer: D

Question # 4
Answer: A

Question # 5
Answer: B