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ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. What does an embedding represent in an LLM?

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

2. Consider applying the meta-prompting technique to generate automated test scripts for API testing. You need to test a REST API endpoint that processes user registration with validation rules. Which one of the following prompts is BEST suited to this task?

A) Role: Act as a test automation engineer with API testing experience. | Context: You are verifying user registration that enforces field and format validation. | Instruction: Generate pytest scripts using requests for both positive (valid) and negative (invalid email, weak password, missing fields) cases. | Input Data: POST /api/register with validation rules for email and password length. | Constraints:
Include fixtures, clear assertions, and naming consistent with pytest. | Output Format: Return complete Python test files.
B) Role: Act as a software engineer. | Context: You are testing registration logic. | Instruction: Create Python scripts to verify endpoint behavior. | Input Data: POST /api/register with test users. | Constraints: Add checks for status codes. | Output Format: Deliver functional scripts.
C) Role: Act as an automation tester. | Context: You are validating an API endpoint. | Instruction: Generate Python test scripts that send POST requests and validate responses. | Input Data: User credentials. | Constraints: Include basic scenarios with asserts. | Output Format: Provide organized scripts.
D) Role: Act as a test automation engineer. | Context: You are creating tests for a registration endpoint. | Instruction: Generate Python test scripts using pytest covering both valid and invalid inputs. | Input Data: POST /api/register with email and password. | Constraints: Follow pytest structure. | Output Format: Provide scripts.

3. 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

4. Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?

A) Meta prompting
B) Zero-shot prompting
C) Few-shot prompting
D) Prompt chaining

5. Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

A) Some GenAI tools may store/process data without explicit consent
B) GenAI outputs can accidentally reveal sensitive information present in inputs
C) Strict GDPR compliance eliminates all privacy risk
D) Using GenAI without regulatory compliance can lead to legal exposure

Solutions:

Question # 1
Answer: D

Question # 2
Answer: A

Question # 3
Answer: C

Question # 4
Answer: C

Question # 5
Answer: C