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| Section | Objectives |
|---|---|
| Organizational Adoption and Governance | - Enterprise GenAI adoption
|
| Application of GenAI in Software Testing | - Practical use in testing workflows
|
| Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
|
| Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
|
| Prompt Engineering for Testing | - Prompt design techniques
|
>> Exam CT-GenAI Objectives <<
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NEW QUESTION # 20
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?
Answer: C
Explanation:
ISO/IEC 42001:2023is the international standard for an AI Management System (AIMS). It is designed to help organizations develop, provide, or use AI systems responsibly by providing a certifiable framework of requirements and controls. In a software testing context, this standard is vital for establishing governance, ensuring that GenAI tools are used consistently and ethically across the lifecycle.NIST AI RMF 1.0(Option B) is a highly respected framework, but it is a set of voluntary guidelines for managing risk, not a
"requirement standard" for a management system.ISO/IEC 23053:2022(Option C) provides a general framework for AI using machine learning but lacks the comprehensive "management system" scope found in
42001. Finally, theEU AI Act(Option D) is a regulation (law), not a technical standard. For a test organization looking to align its GenAI strategy with international best practices and achieve formal certification, ISO/IEC
42001 is the definitive standard to follow, as it covers the organizational processes, data handling, and risk management necessary for high-quality AI operations.
NEW QUESTION # 21
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?
Answer: C
NEW QUESTION # 22
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?
Answer: C
Explanation:
The primary differentiator between an LLM-powered test infrastructure and a traditional chatbot is the move from "deterministic" to "probabilistic" logic. Traditional chatbots (Option D) rely on "if-then" logic, decision trees, and predefined scripts. They can only respond to queries that match specific keywords or patterns mapped in their database. In contrast, an LLM-powered infrastructure utilizes the generative capabilities of Large Language Models to synthesize and create new content based on context. This allows it todynamically generate test insights(Option A)-such as predicting potential regression risks based on unstructured code diffs or drafting test cases for a brand-new feature described in natural language. While traditional bots provide fixed, scripted responses (Option B), LLMs can "reason" through multi-step testing problems and provide nuanced explanations. This contextual awareness is powered by the model's training on vast amounts of technical documentation, enabling it to assist in exploratory testing and complex analysis that traditional, rule-based systems simply cannot handle.
NEW QUESTION # 23
What is a hallucination in LLM outputs?
Answer: A
Explanation:
A hallucination refers to a phenomenon where a Large Language Model generates text that is grammatically correct and seemingly plausible but is factually incorrect or unsupported by the provided context or real-world data. In the context of software testing, this is a critical limitation. For example, an LLM might generate a test case for a software feature that does not exist or cite a non-existent API parameter. These errors occur because LLMs are probabilistic engines designed to predict the "most likely" next token rather than "reasoning" from a set of verified facts. They do not have a built-in "truth" mechanism. While a logical mistake (Option B) is a failure in reasoning and a systematic preference (Option D) describes bias, a hallucination is specifically about the fabrication of information. Testers must be particularly vigilant regarding hallucinations, as they can lead to "false confidence" in test coverage or the creation of invalid bug reports. Mitigations include grounding the model with Retrieval-Augmented Generation (RAG) and implementing rigorous "human-in-the- loop" verification of all AI-generated test artifacts.
NEW QUESTION # 24
Which statement BEST contrasts interaction style and scope?
Answer: C
Explanation:
It is important to distinguish between a general-purposeChatbotand a specializedLLM applicationwithin a testing framework. A chatbot is primarily designed for multi-turn, conversational interactions where the user can ask questions and receive answers in a natural language format. While useful for general queries, it often lacks the specialized workflow integration needed for rigorous testing. Conversely,LLM applications(or
"LLM-powered tools") are built with a specific "scope" in mind, such as automated test generation, code analysis, or requirement mapping. These applications often use the LLM as an underlying engine but surround it with specific UI components, data connectors (like RAG), and fixed task-oriented prompts to achieve a defined testing outcome. While chatbots are "free-form," LLM apps are "capability-driven." This distinction is key for organizations defining a GenAI strategy; simply providing a chatbot to testers is rarely sufficient.
Instead, organizations should develop or adopt LLM applications that integrate directly into the CI/CD pipeline and provide structured, actionable test artifacts that support defined quality engineering tasks.
NEW QUESTION # 25
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