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ISQI CT-GenAI Exam Syllabus Topics:

SectionWeightObjectives
Fundamentals of Generative AI20%- AI Terminology
  • 1. Large Language Models (LLMs)
  • 2. Machine Learning (ML)
  • 3. Artificial Intelligence (AI)
  • 4. Generative AI (GenAI)
  • 5. Deep Learning
  • 6. Tokens and prompts
  • 7. Transformer architecture
- Generative AI Concepts
  • 1. Training data and context windows
  • 2. Hallucinations
  • 3. AI model behavior
  • 4. Emergent capabilities and limitations
  • 5. Alignment and guardrails
  • 6. Model types (Base, Instruction-tuned, RAG)
- AI Development Lifecycle
  • 1. Data collection, preparation, and curation
  • 2. Model training and fine-tuning
  • 3. Deployment and monitoring
  • 4. Evaluation
Tools for Testing Generative AI20%- Using Tools for Common Testing Activities
  • 1. Security testing tools
  • 2. Simulation and monitoring tools
  • 3. Model evaluation tools
  • 4. Prompt testing tools
- Testing Tools Overview
  • 1. Categories of GenAI testing tools
  • 2. Selecting appropriate tools for specific testing needs
Testing Activities for Generative AI30%- Model and Output Evaluation
  • 1. Automated evaluation methods
  • 2. Output correctness and quality assessment
  • 3. Checkpoint testing
  • 4. Human evaluation methods
  • 5. Metamorphic testing
- Requirements-Based Testing
  • 1. Functional requirements for AI-based systems
  • 2. Non-functional requirements for AI-based systems
  • 3. AI-related quality requirements
- Traceability and Documentation
  • 1. Documentation requirements for AI testing
  • 2. Test coverage of AI model components
- Prompt-Based Testing
  • 1. Prompt engineering basics
  • 2. Test case design using prompts
  • 3. Test data creation with GenAI
Risks and Testing Challenges for Generative AI30%- Quality Risks Specific to Generative AI
  • 1. Offensive, harmful, or biased content
  • 2. Input sensitivity (prompt brittleness)
  • 3. Incorrect or fabricated outputs (hallucinations)
  • 4. Dependency on external components
  • 5. Inconsistent responses across runs
  • 6. Inappropriate output for the context
- Testing Challenges for Generative AI
  • 1. Regulatory and compliance considerations
  • 2. Complexity of the AI component
  • 3. Test oracle problem
  • 4. Ethical testing concerns
  • 5. Subjectivity of quality assessment
  • 6. Non-deterministic output behavior
  • 7. Coverage challenges

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

1. You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

A) Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
B) Specify that the role is a test architect specializing in CI/CD pipelines.
C) Include references to version control systems like Git in the constraints.
D) Add a step to review the change log for syntax errors before analysis.


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

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


3. Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

A) The type of cloud platform affects processing speed but not total energy draw
B) The number of tokens processed directly determines the carbon intensity of each query
C) The duration of user sessions primarily affects latency but not power efficiency
D) The location of the data center determines model bias and accuracy levels


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

A) Lowering temperature
B) Using a larger context window
C) Increasing learning rate
D) Increasing temperature


5. Which statement BEST contrasts interaction style and scope?

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


Solutions:

Question # 1
Answer: A
Question # 2
Answer: A
Question # 3
Answer: B
Question # 4
Answer: A
Question # 5
Answer: A

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