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

  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Q & A: 42 Questions and Answers

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ISQI CT-GenAI Exam Syllabus Topics:

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

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


2. What are the three key phases in adopting GenAI in a test organization?

A) Prototype; pilot; decommission
B) Discovery; initiation and usage definition; utilization and iteration
C) Planning; execution; sign-off
D) Training; certification; outsourcing


3. Which of the following is NOT a valid form of LLM-driven test data generation?

A) Creating combinatorial data (e.g., pairwise)
B) Creating production database backups
C) Generating synthetic datasets
D) Setting boundary values


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

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


5. 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) i, ii, iii (Duplicate entry in original source)
B) ii, iii, iv
C) i, ii, iii
D) i, iii, v


Solutions:

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

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