ISQI CT-GenAI real exam prep : ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Updated: Aug 19, 2026
  • Q&As: 42 Questions and Answers

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CT-GenAI exam dumps

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ISQI CT-GenAI Exam Syllabus Topics:

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

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

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

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


2. What defines a prompt pattern in the context of structured GenAI capability building?

A) Using ad hoc prompts without reference to previously proven structures or examples
B) Treating prompts as access credentials or compliance records rather than functional templates
C) Applying a reusable and structured template that guides GenAI models toward consistent outputs
D) Maintaining static documentation repositories without real-time prompt standardization processes


3. 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) Include references to version control systems like Git in the constraints.
C) Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
D) Add a step to review the change log for syntax errors before analysis.


4. 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 replaces the model's general knowledge entirely and prevents overfitting
C) It adapts a pre-trained model to a domain using task-specific data
D) It enhances relevance to organizational terminology and formats


5. 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) A/B testing of prompts
B) Integrating user feedback
C) Iterative prompt modification
D) Output analysis


Solutions:

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

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