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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations - AI agents and integration with test tools |
| 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: Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts |
| Topic 4: Deploying and Integrating GenAI in Test Organisations | 15% | - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness - Measuring value and continuous improvement |
| Topic 5: 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 |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
1. Which statement about fine-tuning for test tasks is INCORRECT?
A) It enhances relevance to organizational terminology and formats
B) It replaces the model's general knowledge entirely and prevents overfitting
C) It can be applied to smaller SLMs to improve task performance with lower compute
D) It adapts a pre-trained model to a domain using task-specific data
2. Which AI approach requires feature engineering and structured data preparation?
A) Symbolic AI
B) Generative AI
C) Deep Learning
D) Classical Machine Learning
3. What defines a prompt pattern in the context of structured GenAI capability building?
A) Maintaining static documentation repositories without real-time prompt standardization processes
B) Using ad hoc prompts without reference to previously proven structures or examples
C) Treating prompts as access credentials or compliance records rather than functional templates
D) Applying a reusable and structured template that guides GenAI models toward consistent outputs
4. Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?
A) Configuring network routers
B) Designing custom CPU instructions
C) Mastering prompt engineering
D) Writing low-level device drivers
5. Which statement BEST contrasts interaction style and scope?
A) Chatbots require API integration; LLM apps do not.
B) Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.
C) Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing
D) Both are identical aside from UI theme.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: B |
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