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ISQI CT-GenAI Exam Syllabus Topics:

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

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

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

2. Which statement BEST contrasts interaction style and scope?

A) Both are identical aside from UI theme.
B) Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing
C) Chatbots require API integration; LLM apps do not.
D) Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.

3. What does an embedding represent in an LLM?

A) Logical rules for reasoning
B) Tokens grouped into context windows
C) A set of test cases for validation
D) Numerical vectors capturing semantic relationships

4. Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

A) EU AI Act
B) ISO/IEC 42001:2023
C) ISO/IEC 23053:2022
D) NIST AI RMF 1.0

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

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

Solutions:

Question # 1
Answer: D

Question # 2
Answer: D

Question # 3
Answer: D

Question # 4
Answer: B

Question # 5
Answer: A