Exam Code: CT-GenAI
Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
Certification Provider: ISQI
Corresponding Certification: AI Testing

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ISQI CT-GenAI Exam Syllabus Topics:

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

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

Question #1

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

Answer: C