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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploying and Integrating GenAI in Test Organisations | 15% | - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness - Measuring value and continuous improvement |
| Topic 2: LLM-Powered Test Infrastructure | 10% | - Architecture and deployment considerations - RAG, fine-tuning, and model adaptation - AI agents and integration with test tools |
| Topic 3: 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 |
| Topic 4: 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 5: 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 |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
1. How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?
A) Transitioning from manual execution to complete automation with no human oversight
B) Replacing existing test coverage validation with automated summary reports generated by AI
C) Shifting from test execution toward reviewing, refining, and validating AI-generated testware
D) Moving from black-box exploratory testing toward exclusively performing code-based white-box checks
2. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
A) Use of a conversational tone and improved response personalization
B) Ability to trigger automated actions beyond conversation
C) Reliance on predefined templates to generate short, factual answers
D) Ability to respond to prompts without explicit user instructions
3. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
A) Use of a conversational tone and improved response personalization
B) Ability to trigger automated actions beyond conversation
C) Reliance on predefined templates to generate short, factual answers
D) Ability to respond to prompts without explicit user instructions
4. Which of the following is NOT a valid form of LLM-driven test data generation?
A) Creating combinatorial data (e.g., pairwise)
B) Generating synthetic datasets
C) Creating production database backups
D) Setting boundary values
5. The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?
A) Precision for anomaly identification and recall for coverage of valid/invalid partitions
B) Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
C) Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
D) Diversity for anomaly identification and precision for partitions
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: A |
