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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: Introduction to Generative AI for Software Testing | 15% | - Capabilities and limitations relevant to testing - Use cases across the testing lifecycle - Core concepts: Generative AI, LLMs, foundation models |
| Topic 3: Deploying and Integrating GenAI in Test Organisations | 15% | - Roles, skills, and team readiness - Strategy, governance, and adoption roadmap - Measuring value and continuous improvement |
| Topic 4: LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations - AI agents and integration with test tools |
| Topic 5: Prompt Engineering for Effective Software Testing | 35% | - Principles and structure of effective prompts - Iterative refinement and evaluation of prompts - Prompt patterns for test design, data generation, automation |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
Which technique MOST directly reduces hallucinations by grounding the model in project realities?
- A. Randomize prompts each run
- B. Provide detailed context
- C. Rely on generic examples only
- D. Use longer temperature settings
Correct Answer: B 🗳️
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Which of the following is NOT a valid form of LLM-driven test data generation?
- A. Generating synthetic datasets
- B. Setting boundary values
- C. Creating production database backups
- D. Creating combinatorial data (e.g., pairwise)
Correct Answer: C 🗳️
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What is a hallucination in LLM outputs?
- A. Generation of factually incorrect content for the task
- B. A systematic preference learned from data
- C. A logical mistake in multi-step deduction
- D. A transient network failure during inference
Correct Answer: A 🗳️
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A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?
- A. Output analysis
- B. Integrating user feedback
- C. A/B testing of prompts
- D. Iterative prompt modification
Correct Answer: C 🗳️
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Which statement BEST describes vision-language models (VLMs)?
- A. VLMs are a subset of multimodal LLMs integrating visual and textual information.
- B. VLMs are unrelated to multimodal LLMs and focus only on UI automation.
- C. VLMs are a superset of multimodal LLMs.
- D. VLMs process audio and video but not images.
Correct Answer: A 🗳️
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