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ISQI CT-GenAI Exam Syllabus Topics:
Section
Weight
Objectives
Fundamentals of Generative AI
20%
- Generative AI Concepts
1. AI model behavior
2. Emergent capabilities and limitations
3. Training data and context windows
4. Hallucinations
5. Alignment and guardrails
6. Model types (Base, Instruction-tuned, RAG)
- AI Terminology
1. Artificial Intelligence (AI)
2. Deep Learning
3. Machine Learning (ML)
4. Transformer architecture
5. Generative AI (GenAI)
6. Large Language Models (LLMs)
7. Tokens and prompts
- AI Development Lifecycle
1. Evaluation
2. Data collection, preparation, and curation
3. Deployment and monitoring
4. Model training and fine-tuning
Risks and Testing Challenges for Generative AI
30%
- Quality Risks Specific to Generative AI
1. Inconsistent responses across runs
2. Input sensitivity (prompt brittleness)
3. Incorrect or fabricated outputs (hallucinations)
4. Offensive, harmful, or biased content
5. Dependency on external components
6. Inappropriate output for the context
- Testing Challenges for Generative AI
1. Non-deterministic output behavior
2. Ethical testing concerns
3. Complexity of the AI component
4. Regulatory and compliance considerations
5. Test oracle problem
6. Subjectivity of quality assessment
7. Coverage challenges
Tools for Testing Generative AI
20%
- Using Tools for Common Testing Activities
1. Simulation and monitoring tools
2. Prompt testing tools
3. Model evaluation tools
4. Security testing tools
- Testing Tools Overview
1. Categories of GenAI testing tools
2. Selecting appropriate tools for specific testing needs
Testing Activities for Generative AI
30%
- Model and Output Evaluation
1. Checkpoint testing
2. Metamorphic testing
3. Automated evaluation methods
4. Output correctness and quality assessment
5. Human evaluation methods
- Requirements-Based Testing
1. Functional requirements for AI-based systems
2. AI-related quality requirements
3. Non-functional requirements for AI-based systems
- Prompt-Based Testing
1. Prompt engineering basics
2. Test case design using prompts
3. Test data creation with GenAI
- Traceability and Documentation
1. Test coverage of AI model components
2. Documentation requirements for AI testing
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
What does an embedding represent in an LLM?
A. A set of test cases for validation
B. Logical rules for reasoning
C. Tokens grouped into context windows
D. Numerical vectors capturing semantic relationships
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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 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.
B. 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.
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.
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You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?
A. Specify that the role is a test analyst with expertise in predictive analytics and risk management.
B. Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
C. Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
D. Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
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You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?
A. Generate conditions -> prioritize by risk -> map to requirements to find gaps
B. Prioritize requirements -> generate conditions -> review defects
C. Detect requirement defects -> generate conditions -> prioritize
D. Generate prioritized conditions in one shot -> verify coverage
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discussion
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