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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Tools for Testing Generative AI | 20% | - Testing Tools Overview
|
| Topic 2: Risks and Testing Challenges for Generative AI | 30% | - Quality Risks Specific to Generative AI
|
| Topic 3: Fundamentals of Generative AI | 20% | - AI Terminology
|
| Topic 4: Testing Activities for Generative AI | 30% | - Prompt-Based Testing
|
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NEW QUESTION # 12
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?
Answer: A
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 # 13
What is a key data-related aspect when defining a GenAI strategy for testing?
Answer: B
Explanation:
A successful Generative AI strategy for testing is heavily dependent on the quality of the data used for grounding (RAG) and prompting. The principle of "Garbage In, Garbage Out" is magnified with LLMs; therefore, a key strategic pillar is the prioritization of accurate, relevant, and high-quality input data. This involves establishing defined quality procedures to ensure that the requirements, codebases, and historical defect logs fed into the model are "clean" and representative of the current system state. Strategy must avoid the "unfiltered" approach (Option C), as including contradictory or obsolete data can lead to hallucinations or irrelevant test cases. While synthetic data (Option D) is a powerful tool for privacy, it cannot entirely replace the nuanced reality found in secured enterprise data. Furthermore, legacy data (Option A) often contains valuable insights for regression testing. Consequently, the strategy should focus on building a robust data pipeline that ensures only verified, contextually appropriate information is utilized, thereby increasing the reliability of AI-generated testware and ensuring it aligns with the organization's quality standards.
NEW QUESTION # 14
You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement
Answer: A
Explanation:
A Retrieval-Augmented Generation (RAG) workflow is designed to "ground" an LLM's output in specific, verifiable data. The logical flow begins with an initial input or "focused query" (Step iv) that defines the tester's goal-in this case, generating cases for a new payments rule. The system then uses this query to perform a semantic search in avector database(Step i) to find the most relevant "chunks" of the new API specification. Following this, the system retrieves complementary data from therelational database(Step iii), such as historical test cases that might provide structural patterns or regression context. Finally, all the retrieved information-the new specs and the historical context-is bundled together and "fed" into the LLM as part of an augmented prompt (Step ii). This ensures the LLM doesn't hallucinate rules but instead synthesizes the new requirements with established organizational testing standards. Following the order in Option B ensures that the model is provided with the most relevant and logically organized context prior to generating the final testware.
NEW QUESTION # 15
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?
Answer: A
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 # 16
Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?
Answer: B
Explanation:
A mature GenAI strategy for software testing must move beyond "hype" and focus on tangible value and operational feasibility. Selecting an LLM based onmeasurable test outcomes(such as reduction in test design time, increase in defect detection, or script accuracy) ensures that the AI investment directly supports the organization's Quality Assurance goals. Furthermore, the model must becompatible with current infrastructure. This includes considerations for data security (on-prem vs. cloud), API integration capabilities, and cost-per-token efficiency. While vendor visibility (Option A) can be a factor, it is not a guarantee of task-specific performance. Prioritizing creativity over compliance (Option B) is highly risky for testing, where precision and policy adherence are paramount. Similarly, while broad functionality (Option C) is useful, it often results in "jack-of-all-trades" models that may not perform as well as specialized or instruction-tuned models on specific testing tasks. Strategic alignment requires a balance between model performance, organizational security requirements, and clear KPIs.
NEW QUESTION # 17
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