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ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. A prompt begins: "You are a senior test manager responsible for risk-based test planning on a payments platform." Which component is this?

A) Instruction
B) Constraints
C) Role
D) Context

2. You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement

A) iii -> iv -> i -> ii
B) iv -> i -> iii -> ii
C) iv -> iii -> i -> ii
D) i -> iv -> iii -> ii

3. What is a primary compliance concern related to Shadow AI in organizational test environments?

A) Violation of established data handling and regulatory compliance standards
B) Difficulty in aligning project milestones with business outcomes
C) Failure to update system documentation within the test process
D) Automated compliance validation during AI tool deployment

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

5. What is a key data-related aspect when defining a GenAI strategy for testing?

A) Neglect legacy data sources as they provide limited immediate relevance to testing tasks
B) Prioritize accurate and relevant input data secured through defined quality procedures
C) Use only auto-generated synthetic data to avoid dependency on enterprise repositories
D) Aggregate data from all available organizational repositories without filtration

Solutions:

Question # 1
Answer: C

Question # 2
Answer: B

Question # 3
Answer: A

Question # 4
Answer: D

Question # 5
Answer: B