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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing Risks of Generative AI in Software Testing | 25% | - Hallucinations, bias, inaccuracy, and consistency risks - Data privacy, security, and compliance concerns - Validation, verification, and mitigation strategies |
| Topic 2: Deploying and Integrating GenAI in Test Organisations | 15% | - Roles, skills, and team readiness - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap |
| Topic 3: Introduction to Generative AI for Software Testing | 15% | - Core concepts: Generative AI, LLMs, foundation models - Capabilities and limitations relevant to testing - Use cases across the testing lifecycle |
| Topic 4: LLM-Powered Test Infrastructure | 10% | - AI agents and integration with test tools - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations |
| Topic 5: Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Principles and structure of effective prompts - Prompt patterns for test design, data generation, automation |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
1. 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) Dataset bias toward UI features
B) Reasoning error in risk calculation logic
C) No defect; this is acceptable
D) Hallucination
2. A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?
A) Malicious code generation
B) Data exfiltration
C) Request manipulation
D) Data poisoning
3. Which AI approach requires feature engineering and structured data preparation?
A) Generative AI
B) Classical Machine Learning
C) Deep Learning
D) Symbolic AI
4. 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 -> i -> iii -> ii
B) i -> iv -> iii -> ii
C) iv -> iii -> i -> ii
D) iii -> iv -> i -> ii
5. How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?
A) Replacing existing test coverage validation with automated summary reports generated by AI
B) Moving from black-box exploratory testing toward exclusively performing code-based white-box checks
C) Transitioning from manual execution to complete automation with no human oversight
D) Shifting from test execution toward reviewing, refining, and validating AI-generated testware
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: D |
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