Exam Code: CT-GenAI
Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
Certification Provider: ISQI
Corresponding Certification: AI Testing
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ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

A) It dynamically generates test insights using contextual information
B) It focuses primarily on visual dashboards and user navigation features
C) It provides fixed responses from predefined rule sets and scripts
D) It produces scripted conversational responses similar to traditional bots


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

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


3. Which setting can reduce variability by narrowing the sampling distribution during inference?

A) Increasing temperature
B) Increasing learning rate
C) Lowering temperature
D) Using a larger context window


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


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) Reasoning error in risk calculation logic
D) Dataset bias toward UI features


Solutions:

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

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