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

  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Updated: Aug 19, 2026
  • Q & A: 42 Questions and Answers

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ISQI CT-GenAI Exam Syllabus Topics:

SectionWeightObjectives
Testing Activities for Generative AI30%- Requirements-Based Testing
  • 1. Non-functional requirements for AI-based systems
  • 2. AI-related quality requirements
  • 3. Functional requirements for AI-based systems
- 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. Prompt engineering basics
  • 3. Test case design using prompts
- Model and Output Evaluation
  • 1. Human evaluation methods
  • 2. Metamorphic testing
  • 3. Checkpoint testing
  • 4. Automated evaluation methods
  • 5. Output correctness and quality assessment
Risks and Testing Challenges for Generative AI30%- Quality Risks Specific to Generative AI
  • 1. Inconsistent responses across runs
  • 2. Offensive, harmful, or biased content
  • 3. Dependency on external components
  • 4. Inappropriate output for the context
  • 5. Incorrect or fabricated outputs (hallucinations)
  • 6. Input sensitivity (prompt brittleness)
- Testing Challenges for Generative AI
  • 1. Coverage challenges
  • 2. Regulatory and compliance considerations
  • 3. Subjectivity of quality assessment
  • 4. Ethical testing concerns
  • 5. Complexity of the AI component
  • 6. Non-deterministic output behavior
  • 7. Test oracle problem
Tools for Testing Generative AI20%- Using Tools for Common Testing Activities
  • 1. Model evaluation tools
  • 2. Security testing tools
  • 3. Simulation and monitoring tools
  • 4. Prompt testing tools
- Testing Tools Overview
  • 1. Selecting appropriate tools for specific testing needs
  • 2. Categories of GenAI testing tools
Fundamentals of Generative AI20%- Generative AI Concepts
  • 1. Model types (Base, Instruction-tuned, RAG)
  • 2. AI model behavior
  • 3. Training data and context windows
  • 4. Emergent capabilities and limitations
  • 5. Hallucinations
  • 6. Alignment and guardrails
- AI Development Lifecycle
  • 1. Evaluation
  • 2. Data collection, preparation, and curation
  • 3. Deployment and monitoring
  • 4. Model training and fine-tuning
- AI Terminology
  • 1. Tokens and prompts
  • 2. Deep Learning
  • 3. Generative AI (GenAI)
  • 4. Artificial Intelligence (AI)
  • 5. Large Language Models (LLMs)
  • 6. Transformer architecture
  • 7. Machine Learning (ML)

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

1. 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) Prioritize accurate and relevant input data secured through defined quality procedures
C) Use only auto-generated synthetic data to avoid dependency on enterprise repositories
D) Neglect legacy data sources as they provide limited immediate relevance to testing tasks


2. What is a primary compliance concern related to Shadow AI in organizational test environments?

A) Automated compliance validation during AI tool deployment
B) Violation of established data handling and regulatory compliance standards
C) Failure to update system documentation within the test process
D) Difficulty in aligning project milestones with business outcomes


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

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


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

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


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


Solutions:

Question # 1
Answer: B
Question # 2
Answer: B
Question # 3
Answer: B
Question # 4
Answer: A
Question # 5
Answer: D

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