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ISQI CT-GenAI Exam Syllabus Topics:

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

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

Question #1

A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

A. Malicious code generation
B. Data exfiltration
C. Request manipulation
D. Data poisoning

Answer: C

Explanation: Only visible for TestPassKing members. You can sign-up / login (it's free).

Question #2

Which technique MOST directly reduces hallucinations by grounding the model in project realities?

A. Randomize prompts each run
B. Provide detailed context
C. Use longer temperature settings
D. Rely on generic examples only

Answer: B

Explanation: Only visible for TestPassKing members. You can sign-up / login (it's free).

Question #3

How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

A. Moving from black-box exploratory testing toward exclusively performing code-based white-box checks
B. Replacing existing test coverage validation with automated summary reports generated by AI
C. Shifting from test execution toward reviewing, refining, and validating AI-generated testware
D. Transitioning from manual execution to complete automation with no human oversight

Answer: C

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Question #4

Which setting can reduce variability by narrowing the sampling distribution during inference?