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: Jul 30, 2026
  • Q & A: 42 Questions and Answers

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ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

A) Some GenAI tools may store/process data without explicit consent
B) Using GenAI without regulatory compliance can lead to legal exposure
C) GenAI outputs can accidentally reveal sensitive information present in inputs
D) Strict GDPR compliance eliminates all privacy risk


2. A prompt section states: "Web checkout module v3.2; focus on coupon application; existing regression suite IDs T-112-T-150; recent defect ID BUG-431." Which component is this?

A) Input data
B) Instruction
C) Output format
D) Constraints


3. Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

A) ISO/IEC 42001:2023
B) EU AI Act
C) ISO/IEC 23053:2022
D) NIST AI RMF 1.0


4. In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

A) i, iii, v
B) i, ii, iii (Duplicate entry in original source)
C) ii, iii, iv
D) i, ii, iii


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

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


Solutions:

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

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