ISQI CT-GenAI : 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: Oct 06, 2026
Q & A: 42 Questions and Answers

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ISQI CT-GenAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Fundamentals of Generative AI	20%	- Generative AI Concepts 1. Alignment and guardrails 2. Hallucinations 3. Model types (Base, Instruction-tuned, RAG) 4. Training data and context windows 5. AI model behavior 6. Emergent capabilities and limitations - AI Terminology 1. Transformer architecture 2. Large Language Models (LLMs) 3. Deep Learning 4. Generative AI (GenAI) 5. Artificial Intelligence (AI) 6. Machine Learning (ML) 7. Tokens and prompts - AI Development Lifecycle 1. Model training and fine-tuning 2. Data collection, preparation, and curation 3. Evaluation 4. Deployment and monitoring
Topic 2: Testing Activities for Generative AI	30%	- Traceability and Documentation 1. Test coverage of AI model components 2. Documentation requirements for AI testing - Model and Output Evaluation 1. Human evaluation methods 2. Automated evaluation methods 3. Checkpoint testing 4. Metamorphic testing 5. Output correctness and quality assessment - Prompt-Based Testing 1. Test case design using prompts 2. Prompt engineering basics 3. Test data creation with GenAI - Requirements-Based Testing 1. Non-functional requirements for AI-based systems 2. Functional requirements for AI-based systems 3. AI-related quality requirements
Topic 3: Tools for Testing Generative AI	20%	- Using Tools for Common Testing Activities 1. Model evaluation tools 2. Simulation and monitoring tools 3. Security testing tools 4. Prompt testing tools - Testing Tools Overview 1. Selecting appropriate tools for specific testing needs 2. Categories of GenAI testing tools
Topic 4: Risks and Testing Challenges for Generative AI	30%	- Testing Challenges for Generative AI 1. Coverage challenges 2. Subjectivity of quality assessment 3. Non-deterministic output behavior 4. Test oracle problem 5. Regulatory and compliance considerations 6. Ethical testing concerns 7. Complexity of the AI component - Quality Risks Specific to Generative AI 1. Inconsistent responses across runs 2. Dependency on external components 3. Offensive, harmful, or biased content 4. Inappropriate output for the context 5. Incorrect or fabricated outputs (hallucinations) 6. Input sensitivity (prompt brittleness)

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

Question #1

Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

A. The duration of user sessions primarily affects latency but not power efficiency
B. The type of cloud platform affects processing speed but not total energy draw
C. The location of the data center determines model bias and accuracy levels
D. The number of tokens processed directly determines the carbon intensity of each query

Reveal Solution Discussion 0

Correct Answer: D 🗳️