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ISQI CT-GenAI Exam Syllabus Topics:

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

A) ISO/IEC 23053:2022
B) EU AI Act
C) NIST AI RMF 1.0
D) ISO/IEC 42001:2023


2. A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?

A) A/B testing of prompts
B) Iterative prompt modification
C) Output analysis
D) Integrating user feedback


3. Which statement BEST contrasts interaction style and scope?

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


4. The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

A) Diversity for anomaly identification and precision for partitions
B) Precision for anomaly identification and recall for coverage of valid/invalid partitions
C) Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
D) Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions


5. You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement

A) iv -> i -> iii -> ii
B) iii -> iv -> i -> ii
C) iv -> iii -> i -> ii
D) i -> iv -> iii -> ii


Solutions:

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

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