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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering for Effective Software Testing | 35% | - Prompt patterns for test design, data generation, automation - Iterative refinement and evaluation of prompts - Principles and structure of effective prompts |
| Topic 2: Introduction to Generative AI for Software Testing | 15% | - Use cases across the testing lifecycle - Core concepts: Generative AI, LLMs, foundation models - Capabilities and limitations relevant to testing |
| Topic 3: LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - AI agents and integration with test tools - Architecture and deployment considerations |
| Topic 4: Deploying and Integrating GenAI in Test Organisations | 15% | - Roles, skills, and team readiness - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap |
| Topic 5: Managing Risks of Generative AI in Software Testing | 25% | - Hallucinations, bias, inaccuracy, and consistency risks - Validation, verification, and mitigation strategies - Data privacy, security, and compliance concerns |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
1. Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?
A) Zero-shot prompting
B) Prompt chaining
C) Meta prompting
D) Few-shot prompting
2. Who typically defines the system prompt in a testing workflow?
A) A tester configuring the assistant
B) CI server automatically without human input
C) End user during normal chat use
D) Product owner in user stories only
3. Which concept refers to breaking text into smaller units for processing by LLMs?
A) Tokenization
B) Context Window
C) Embeddings
D) Transformer
4. Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?
A) Configuring network routers
B) Mastering prompt engineering
C) Writing low-level device drivers
D) Designing custom CPU instructions
5. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
A) Ability to respond to prompts without explicit user instructions
B) Use of a conversational tone and improved response personalization
C) Ability to trigger automated actions beyond conversation
D) Reliance on predefined templates to generate short, factual answers
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: C |
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