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ISQI CT-GenAI Exam Syllabus Topics:

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

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

Question 1

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

Question 2

Which statement BEST contrasts interaction style and scope?

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

Question 3

An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?

A. Dataset bias toward UI features
B. No defect; this is acceptable
C. Hallucination
D. Reasoning error in risk calculation logic

Question 4

In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

A. i, iii, v
B. i, ii, iii (Duplicate entry in original source)
C. ii, iii, iv
D. i, ii, iii

Question 5

What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Solutions:

Question 1
Answer: C

Question 2
Answer: B

Question 3
Answer: D

Question 4
Answer: D

Question 5
Answer: C