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

  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Updated: Aug 04, 2026
  • Q&As: 42 Questions and Answers

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ISQI CT-GenAI Exam Syllabus Topics:

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

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 mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
C) Include references to version control systems like Git in the constraints.
D) Add a step to review the change log for syntax errors before analysis.


2. Which statement BEST contrasts interaction style and scope?

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


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

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


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


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) i -> iv -> iii -> ii
D) iv -> iii -> i -> ii


Solutions:

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

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