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ISQI CT-GenAI Exam Syllabus Topics:

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

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

Question #1

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. Instruction
  • B. Constraints
  • C. Output format
  • D. Input data
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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

Question #2

Who typically defines the system prompt in a testing workflow?

  • A. CI server automatically without human input
  • B. A tester configuring the assistant
  • C. Product owner in user stories only
  • D. End user during normal chat use
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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

Question #3

What does an embedding represent in an LLM?

  • A. Numerical vectors capturing semantic relationships
  • B. Tokens grouped into context windows
  • C. A set of test cases for validation
  • D. Logical rules for reasoning
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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

Question #4

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. Output analysis
  • B. Iterative prompt modification
  • C. Integrating user feedback
  • D. A/B testing of prompts
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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

Question #5

Which technique MOST directly reduces hallucinations by grounding the model in project realities?

  • A. Provide detailed context
  • B. Randomize prompts each run
  • C. Use longer temperature settings
  • D. Rely on generic examples only
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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

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