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Exam Code: CT-GenAI
Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
Certification Provider: ISQI
Corresponding Certification: AI Testing
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ISQI CT-GenAI Exam Syllabus Topics:

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

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

An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?

  • A. Reasoning error in risk calculation logic
  • B. Dataset bias toward UI features
  • C. No defect; this is acceptable
  • D. Hallucination
Answer: A

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

What is a primary compliance concern related to Shadow AI in organizational test environments?

  • A. Difficulty in aligning project milestones with business outcomes
  • B. Violation of established data handling and regulatory compliance standards
  • C. Automated compliance validation during AI tool deployment
  • D. Failure to update system documentation within the test process
Answer: B

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Which concept refers to breaking text into smaller units for processing by LLMs?

  • A. Transformer
  • B. Context Window
  • C. Embeddings
  • D. Tokenization
Answer: D

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

Which statement BEST contrasts interaction style and scope?

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

Explanation: Only visible for TrainingDump members. You can sign-up / login (it's free).

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

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