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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Managing Risks of Generative AI in Software Testing | 25% | - Validation, verification, and mitigation strategies - Data privacy, security, and compliance concerns - Hallucinations, bias, inaccuracy, and consistency risks |
| Topic 3: Deploying and Integrating GenAI in Test Organisations | 15% | - Roles, skills, and team readiness - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap |
| Topic 4: LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations - AI agents and integration with test tools |
| Topic 5: 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 |
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問題 #41
What are the three key phases in adopting GenAI in a test organization?
答案：D
解題說明：
According to the strategic frameworks for AI adoption (as detailed in the CT-GenAI and related ISO/IEC
42001 standards), the journey toward organizational AI maturity follows three primary phases. TheDiscovery phase involves identifying potential use cases, assessing current technical readiness, and understanding the legal/risk landscape. TheInitiation and Usage Definitionphase is where the organization sets the "ground rules"-defining which tools are approved, establishing system prompts, creating prompt libraries, and training the staff on prompt engineering. This phase transitions the AI from a novelty into a structured capability. Finally, theUtilization and Iterationphase is the ongoing process where GenAI is used in daily testing activities, and its outputs are constantly monitored, measured, and improved through feedback loops.
This ensures the strategy remains dynamic and adapts to new model capabilities or changing project requirements. Options B, C, and D represent standard project management or IT lifecycles but do not capture the specific "learning and refinement" nature required for successful Generative AI integration in a testing department.
問題 #42
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
答案：B
解題說明：
While a basic chatbot is primarily designed for textual interaction and information retrieval, anLLM- powered agent(or AI Agent) is characterized by itsagency-the ability to use tools and trigger actions in the external world. In a software testing context, an agent does not just "talk" about testing; it can actually perform testing tasks. For example, an agent could be given the goal to "verify the login module," and it would independently decide to call an API, generate a test script, execute it against a test environment, and then analyze the results to report a bug in Jira. This ability totrigger automated actions(Option C) through
"function calling" or tool integration is what makes agents far more powerful than simple conversational interfaces (Option D). Agents can reason about "how" to achieve a goal, selecting the appropriate tools (like Selenium, Postman, or specialized internal utilities) to complete the task. This moves the AI from being a passive advisor to an active participant in the test automation ecosystem, requiring testers to focus more on goal definition and result validation.
問題 #43
What defines a prompt pattern in the context of structured GenAI capability building?
答案：A
解題說明：
In the context of structured Generative AI capability building, a prompt pattern is a formalized method of interaction that ensures repeatability and reliability. Much like software design patterns, prompt patterns provide a reusable and structured template designed to guide Large Language Models (LLMs) toward producing specific, high-quality, and consistent outputs. Without these patterns, testers often rely on "zero- shot" or ad hoc prompting, which frequently leads to non-deterministic results that are difficult to validate in a professional testing lifecycle. By adopting prompt patterns, organizations can standardize how requirements are translated into test cases or how code is analyzed for defects. This standardization is critical for scaling GenAI across a team, as it allows for the creation of a "prompt library" where successful structures-such as Persona-based, Few-shot, or Chain-of-Thought patterns-are documented and reused. This approach moves the use of GenAI from a trial-and-error activity to a disciplined engineering practice, ensuring that the model understands the specific context, constraints, and expected output formats required for rigorous software testing tasks.
問題 #44
An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?
答案：D
解題說明：
This scenario describes a specialized form ofData Exfiltration(specifically targeting the model's internal
"weights" or training memory). While data exfiltration usually refers to stealing data from a database, in the context of LLMs, it can also refer to techniques that force the model to "reveal" sensitive information it was trained on or data that exists within its current context window. By using long, repetitive, or specifically
"crafted" prompts to overwhelm the model's normal attention mechanisms or safety filters, an attacker may cause the model to output verbatim snippets of proprietary information, PII, or internal documentation that should have remained confidential. This is different fromRequest Manipulation(Option D), which aims to change the model's behavior, orData Poisoning(Option A), which happens during training. In testing, this risk is high when models are fine-tuned on private company repositories. Testers must be aware that if a model is accessible to unauthorized users, those users might use adversarial prompting techniques to extract sensitive code or business logic through these types of data leakage attacks.
問題 #45
What BEST protects sensitive test data at rest and in transit?
答案：D
解題說明：
Data security is a paramount concern when using GenAI in testing, as test environments often contain sensitive business logic or PII (Personally Identifiable Information). To protect this data "at rest" (stored in databases or vector stores) and "in transit" (being sent to the LLM), a combination of technical controls is required.Role-Based Access Control (RBAC)is a fundamental security pillar that ensures only authorized individuals or services can access specific datasets or trigger GenAI workflows. This prevents unauthorized users from feeding sensitive enterprise data into public AI models. While encryption (omitted in Option A as an alternative to obfuscation) and TLS (falsely suggested to be disabled in Option C) are essential technical layers for protecting data in transit, RBAC provides the organizational "gatekeeping" necessary to manage who can interact with the AI system. In a professional GenAI strategy, testers must ensure that the tools they use adhere to strict access policies, ensuring that the "Input Data" used for prompting remains within the secured organizational boundary and is not leaked to unauthorized entities or public training sets.
問題 #46
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