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Updated: Aug 19, 2026

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ISQI CT-GenAI Exam Overview:

Certification Vendor:ISQI / ASTQB
Exam Name:ISTQB Certified Tester Testing with Generative AI
Exam Number:CT-GenAI
Exam Format:Multiple Choice
Real Exam Qty:40
Exam Price:250 EUR (+ VAT)
Available Languages:English, German
Exam Duration:60 minutes
Passing Score:65%
Related Certifications:ISTQB CTFL (Foundation Level Tester)
Certificate Validity Period:Not publicly specified (typically 3-5 years depending on scheme)
Sample Questions:ISQI CT-GenAI Sample Questions
Exam Way:Online proctored / In-person at authorized test centers
Pre Condition:ISTQB CTFL (Foundation Level Tester) certification is recommended but not required
Official Syllabus URL:https://www.astqb.org/ct-genai/

ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?

A) Specify that the role is a test analyst with expertise in predictive analytics and risk management.
B) Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
C) Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
D) Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.


2. Which AI approach requires feature engineering and structured data preparation?

A) Symbolic AI
B) Classical Machine Learning
C) Deep Learning
D) Generative AI


3. Who typically defines the system prompt in a testing workflow?

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


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

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


5. Which concept refers to breaking text into smaller units for processing by LLMs?

A) Tokenization
B) Embeddings
C) Transformer
D) Context Window


Solutions:

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

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