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

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

Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

  • A. Designing custom CPU instructions
  • B. Mastering prompt engineering
  • C. Configuring network routers
  • D. Writing low-level device drivers
Answer: B

Explanation: Only visible for ITCertMagic members. You can sign-up / login (it's free).

A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?

  • A. A/B testing of prompts
  • B. Iterative prompt modification
  • C. Integrating user feedback
  • D. Output analysis
Answer: A

Explanation: Only visible for ITCertMagic members. You can sign-up / login (it's free).

When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?

  • A. Maintaining data privacy and minimizing security risks from external services
  • B. Achieving faster responses by reducing model checkpoints and updates
  • C. Focusing primarily on user experience improvements and response formatting
  • D. Maximizing scalability by deploying larger cloud-based LLM clusters
Answer: A

Explanation: Only visible for ITCertMagic members. You can sign-up / login (it's free).

What is a hallucination in LLM outputs?

  • A. A systematic preference learned from data
  • B. A transient network failure during inference
  • C. A logical mistake in multi-step deduction
  • D. Generation of factually incorrect content for the task
Answer: D

Explanation: Only visible for ITCertMagic members. You can sign-up / login (it's free).

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. Hallucination
  • C. Reasoning error in risk calculation logic
  • D. No defect; this is acceptable
Answer: C

Explanation: Only visible for ITCertMagic members. You can sign-up / login (it's free).

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