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Last Updated: Aug 20, 2026

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ISQI CT-GenAI Exam Syllabus Topics:

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

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

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

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


2. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

A) Ability to trigger automated actions beyond conversation
B) Reliance on predefined templates to generate short, factual answers
C) Use of a conversational tone and improved response personalization
D) Ability to respond to prompts without explicit user instructions


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

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


4. Which option BEST differentiates the three prompting techniques?

A) Meta = step decomposition; Chaining = zero-shot only; Few-shot = manual optimization
B) Few-shot = no examples; Chaining = single prompt; Meta = disable iteration
C) Few-shot = examples; Chaining = multi-step prompts; Meta = model helps draft/refine prompts
D) Chaining = give examples; Few-shot = break tasks; Meta = manual edits only


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


Solutions:

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

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