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ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

A) Select open-source models prioritizing creativity over compliance or performance consistency
B) Select LLMs aligned to measurable test outcomes, compatible with current infrastructure
C) Select models with maximum vendor visibility and strong online presence to ensure reliability
D) Select broad-coverage models offering diverse functionalities for various test scenarios


2. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

A) Ability to respond to prompts without explicit user instructions
B) Ability to trigger automated actions beyond conversation
C) Use of a conversational tone and improved response personalization
D) Reliance on predefined templates to generate short, factual answers


3. 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) Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
B) Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
C) Specify that the role is a test analyst with expertise in predictive analytics and risk management.
D) Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.


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

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


5. What does an embedding represent in an LLM?

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


Solutions:

Question # 1
Answer: B
Question # 2
Answer: B
Question # 3
Answer: D
Question # 4
Answer: D
Question # 5
Answer: C

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