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ISQI CT-GenAI Practice Q&A's

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ISQI CT-GenAI Exam Syllabus Topics:

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

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

Question #1

In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

  • A. ii, iii, iv
  • B. i, ii, iii
  • C. i, iii, v
  • D. i, ii, iii (Duplicate entry in original source)
Answer: B

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Question #2

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

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

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Question #3

Which technique MOST directly reduces hallucinations by grounding the model in project realities?

  • A. Use longer temperature settings
  • B. Rely on generic examples only
  • C. Provide detailed context
  • D. Randomize prompts each run
Answer: C

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Question #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. Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
  • D. Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
Answer: B

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Question #5

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

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