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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering for Effective Software Testing | 35% | - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts - Iterative refinement and evaluation of prompts |
| Topic 2: Deploying and Integrating GenAI in Test Organisations | 15% | - Measuring value and continuous improvement - Roles, skills, and team readiness - Strategy, governance, and adoption roadmap |
| 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: Managing Risks of Generative AI in Software Testing | 25% | - Data privacy, security, and compliance concerns - Hallucinations, bias, inaccuracy, and consistency risks - Validation, verification, and mitigation strategies |
| Topic 5: 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 |
1. A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?
A) Request manipulation
B) Data exfiltration
C) Data poisoning
D) Malicious code generation
2. You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?
A) Prioritize requirements -> generate conditions -> review defects
B) Generate prioritized conditions in one shot -> verify coverage
C) Detect requirement defects -> generate conditions -> prioritize
D) Generate conditions -> prioritize by risk -> map to requirements to find gaps
3. Which statement BEST contrasts interaction style and scope?
A) Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing
B) Both are identical aside from UI theme.
C) Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.
D) Chatbots require API integration; LLM apps do not.
4. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
A) Reliance on predefined templates to generate short, factual answers
B) Ability to respond to prompts without explicit user instructions
C) Use of a conversational tone and improved response personalization
D) Ability to trigger automated actions beyond conversation
5. Who typically defines the system prompt in a testing workflow?
A) Product owner in user stories only
B) CI server automatically without human input
C) End user during normal chat use
D) A tester configuring the assistant
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: D |
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