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Updated: Sep 12, 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
Certificate Validity Period:Not publicly specified (typically 3-5 years depending on scheme)
Exam Duration:60 minutes
Exam Format:Multiple Choice
Available Languages:German, English
Related Certifications:ISTQB CTFL (Foundation Level Tester)
Exam Price:250 EUR (+ VAT)
Real Exam Qty:40
Passing Score:65%
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. Test oracle problem
  • 2. Coverage challenges
  • 3. Ethical testing concerns
  • 4. Subjectivity of quality assessment
  • 5. Non-deterministic output behavior
  • 6. Regulatory and compliance considerations
  • 7. Complexity of the AI component
- Quality Risks Specific to Generative AI
  • 1. Offensive, harmful, or biased content
  • 2. Input sensitivity (prompt brittleness)
  • 3. Incorrect or fabricated outputs (hallucinations)
  • 4. Inappropriate output for the context
  • 5. Inconsistent responses across runs
  • 6. Dependency on external components
Topic 2: Fundamentals of Generative AI20%- AI Development Lifecycle
  • 1. Data collection, preparation, and curation
  • 2. Evaluation
  • 3. Model training and fine-tuning
  • 4. Deployment and monitoring
- AI Terminology
  • 1. Generative AI (GenAI)
  • 2. Transformer architecture
  • 3. Artificial Intelligence (AI)
  • 4. Machine Learning (ML)
  • 5. Large Language Models (LLMs)
  • 6. Tokens and prompts
  • 7. Deep Learning
- Generative AI Concepts
  • 1. AI model behavior
  • 2. Emergent capabilities and limitations
  • 3. Model types (Base, Instruction-tuned, RAG)
  • 4. Alignment and guardrails
  • 5. Training data and context windows
  • 6. Hallucinations
Topic 3: Testing Activities for Generative AI30%- Requirements-Based Testing
  • 1. Non-functional requirements for AI-based systems
  • 2. AI-related quality requirements
  • 3. Functional requirements for AI-based systems
- Traceability and Documentation
  • 1. Documentation requirements for AI testing
  • 2. Test coverage of AI model components
- Prompt-Based Testing
  • 1. Test data creation with GenAI
  • 2. Prompt engineering basics
  • 3. Test case design using prompts
- Model and Output Evaluation
  • 1. Human evaluation methods
  • 2. Output correctness and quality assessment
  • 3. Automated evaluation methods
  • 4. Checkpoint testing
  • 5. Metamorphic testing
Topic 4: Tools for Testing Generative AI20%- Using Tools for Common Testing Activities
  • 1. Simulation and monitoring tools
  • 2. Security testing 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

How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

  • A. Transitioning from manual execution to complete automation with no human oversight
  • B. Moving from black-box exploratory testing toward exclusively performing code-based white-box checks
  • C. Shifting from test execution toward reviewing, refining, and validating AI-generated testware
  • D. Replacing existing test coverage validation with automated summary reports generated by AI
Answer: C

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Question #2

You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

  • A. Specify that the role is a test architect specializing in CI/CD pipelines.
  • B. Add a step to review the change log for syntax errors before analysis.
  • C. Include references to version control systems like Git in the constraints.
  • D. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
Answer: D

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Question #3

Which statement BEST contrasts interaction style and scope?

  • A. Chatbots require API integration; LLM apps do not.
  • B. Both are identical aside from UI theme.
  • C. Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing
  • D. Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.
Answer: D

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Question #4

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

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

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Question #5

Which statement BEST describes vision-language models (VLMs)?

  • A. VLMs are a subset of multimodal LLMs integrating visual and textual information.
  • B. VLMs process audio and video but not images.
  • C. VLMs are a superset of multimodal LLMs.
  • D. VLMs are unrelated to multimodal LLMs and focus only on UI automation.
Answer: A

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