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

"ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0", also known as CT-GenAI exam, is a ISQI Certification. With the complete collection of questions and answers, Pass4sureCert has assembled to take you through 42 Q&As to your CT-GenAI Exam preparation. In the CT-GenAI exam resources, you will cover every field and category in AI Testing Certification helping to ready you for your successful ISQI Certification.

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  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Certification Provider: ISQI
  • Corresponding Certification: AI Testing
  • Updated: Sep 12, 2026
  • No. of Questions: 42 Questions & Answers with Testing Engine
  • Download Limit: Unlimited

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

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

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

  • A. Tokenization
  • B. Context Window
  • C. Embeddings
  • D. Transformer
Answer: A

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Which setting can reduce variability by narrowing the sampling distribution during inference?

  • A. Increasing temperature
  • B. Lowering temperature
  • C. Using a larger context window
  • D. Increasing learning rate
Answer: B

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Which statement about fine-tuning for test tasks is INCORRECT?

  • A. It can be applied to smaller SLMs to improve task performance with lower compute
  • B. It adapts a pre-trained model to a domain using task-specific data
  • C. It enhances relevance to organizational terminology and formats
  • D. It replaces the model's general knowledge entirely and prevents overfitting
Answer: D

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

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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. Integrating user feedback
  • B. Iterative prompt modification
  • C. A/B testing of prompts
  • D. Output analysis
Answer: C

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