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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: LLM-Powered Test Infrastructure | 10% | - AI agents and integration with test tools - Architecture and deployment considerations - RAG, fine-tuning, and model adaptation |
| Topic 2: 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 |
| Topic 3: 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 |
| Topic 4: Deploying and Integrating GenAI in Test Organisations | 15% | - Measuring value and continuous improvement - Roles, skills, and team readiness - Strategy, governance, and adoption roadmap |
| Topic 5: 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 |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
1. Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?
A) The number of tokens processed directly determines the carbon intensity of each query
B) The duration of user sessions primarily affects latency but not power efficiency
C) The location of the data center determines model bias and accuracy levels
D) The type of cloud platform affects processing speed but not total energy draw
2. An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?
A) Request manipulation
B) Data exfiltration
C) Data poisoning
D) Malicious code generation
3. When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?
A) Focusing primarily on user experience improvements and response formatting
B) Maximizing scalability by deploying larger cloud-based LLM clusters
C) Achieving faster responses by reducing model checkpoints and updates
D) Maintaining data privacy and minimizing security risks from external services
4. 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
5. What BEST protects sensitive test data at rest and in transit?
A) Enforce role-based access controls
B) Use public file shares with read-only links
C) Disable TLS and rely on VPN only
D) Rely on obfuscation instead of encryption
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: A |
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