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:
| Section | Weight | Objectives |
|---|---|---|
| Introduction to Generative AI for Software Testing | 15% | - Use cases across the testing lifecycle - Capabilities and limitations relevant to testing - Core concepts: Generative AI, LLMs, foundation models |
| Prompt Engineering for Effective Software Testing | 35% | - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts - Iterative refinement and evaluation of prompts |
| LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - AI agents and integration with test tools - Architecture and deployment considerations |
| Managing Risks of Generative AI in Software Testing | 25% | - Data privacy, security, and compliance concerns - Validation, verification, and mitigation strategies - Hallucinations, bias, inaccuracy, and consistency risks |
| Deploying and Integrating GenAI in Test Organisations | 15% | - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness - Measuring value and continuous improvement |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
What is a key data-related aspect when defining a GenAI strategy for testing?
- A. Prioritize accurate and relevant input data secured through defined quality procedures
- B. Neglect legacy data sources as they provide limited immediate relevance to testing tasks
- C. Aggregate data from all available organizational repositories without filtration
- D. Use only auto-generated synthetic data to avoid dependency on enterprise repositories
Correct Answer: A 🗳️
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What defines a prompt pattern in the context of structured GenAI capability building?
- A. Treating prompts as access credentials or compliance records rather than functional templates
- B. Applying a reusable and structured template that guides GenAI models toward consistent outputs
- C. Using ad hoc prompts without reference to previously proven structures or examples
- D. Maintaining static documentation repositories without real-time prompt standardization processes
Correct Answer: B 🗳️
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What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
- A. Reliance on predefined templates to generate short, factual answers
- B. Use of a conversational tone and improved response personalization
- C. Ability to trigger automated actions beyond conversation
- D. Ability to respond to prompts without explicit user instructions
Correct Answer: C 🗳️
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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. Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
- B. Specify that the role is a test analyst with expertise in predictive analytics and risk management.
- 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. Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
Correct Answer: C 🗳️
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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 type of cloud platform affects processing speed but not total energy draw
- C. The duration of user sessions primarily affects latency but not power efficiency
- D. The location of the data center determines model bias and accuracy levels
Correct Answer: A 🗳️
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