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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing Risks of Generative AI in Software Testing | 25% | - Validation, verification, and mitigation strategies - Data privacy, security, and compliance concerns - Hallucinations, bias, inaccuracy, and consistency risks |
| Topic 2: LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations - AI agents and integration with test tools |
| Topic 3: Deploying and Integrating GenAI in Test Organisations | 15% | - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness |
| Topic 4: 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 |
| Topic 5: 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 |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
Which concept refers to breaking text into smaller units for processing by LLMs?
- A. Embeddings
- B. Context Window
- C. Tokenization
- D. Transformer
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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. No defect; this is acceptable
- B. Dataset bias toward UI features
- C. Reasoning error in risk calculation logic
- D. Hallucination
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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 -> iii -> i -> ii
- B. iv -> i -> iii -> ii
- C. i -> iv -> iii -> ii
- D. iii -> iv -> i -> ii
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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 duration of user sessions primarily affects latency but not power efficiency
- C. The type of cloud platform affects processing speed but not total energy draw
- D. The location of the data center determines model bias and accuracy levels
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Who typically defines the system prompt in a testing workflow?
- A. A tester configuring the assistant
- B. Product owner in user stories only
- C. End user during normal chat use
- D. CI server automatically without human input
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