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ISQI CT-GenAI Exam Syllabus Topics:

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

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

Question 1

A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

A. Data exfiltration
B. Malicious code generation
C. Request manipulation
D. Data poisoning


Question 2

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

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


Question 3

Who typically defines the system prompt in a testing workflow?

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


Question 4

What is a hallucination in LLM outputs?

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


Question 5

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

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


Solutions:

Question 1
Answer: C
Question 2
Answer: B
Question 3
Answer: B
Question 4
Answer: D
Question 5
Answer: C

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