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ISQI CT-GenAI Exam Braindumps - in .pdf Free Demo

  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Last Updated: Aug 15, 2026
  • Q & A: 42 Questions and Answers
  • Convenient, easy to study. Printable ISQI CT-GenAI PDF Format. It is an electronic file format regardless of the operating system platform. 100% Money Back Guarantee.
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  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Last Updated: Aug 15, 2026
  • Q & A: 42 Questions and Answers
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ISQI CT-GenAI Exam Syllabus Topics:

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

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 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


2. 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) Ability to trigger automated actions beyond conversation
C) Reliance on predefined templates to generate short, factual answers
D) Use of a conversational tone and improved response personalization


3. 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


4. The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

A) Precision for anomaly identification and recall for coverage of valid/invalid partitions
B) Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
C) Diversity for anomaly identification and precision for partitions
D) Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions


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


Solutions:

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

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