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| Section | Objectives |
|---|---|
| Topic 1: Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
|
| Topic 2: Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
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| Topic 3: Organizational Adoption and Governance | - Enterprise GenAI adoption
|
| Topic 4: Prompt Engineering for Testing | - Prompt design techniques
|
| Topic 5: Application of GenAI in Software Testing | - Practical use in testing workflows
|
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NEW QUESTION # 10
Which AI approach requires feature engineering and structured data preparation?
Answer: D
Explanation:
Classical Machine Learning(which includes algorithms like Random Forests, Support Vector Machines, and Linear Regression) is characterized by its reliance onFeature Engineering. This is the process where human experts manually select, extract, and transform raw data into a set of "features" or variables that the algorithm can process. For instance, in a classical ML model predicting software defects, a tester might have to manually define features like "lines of code changed" or "number of previous bugs." In contrast,Deep Learningand its subset,Generative AI(Options B and D), utilize "Representation Learning." This means the multi-layered neural networks automatically identify and extract the relevant features from raw, often unstructured data (like text or images) without explicit human instruction.Symbolic AI(Option A) is based on hard-coded logical rules rather than data-driven learning. Understanding this distinction is fundamental for testers, as it determines the level of data preparation required: Classical ML requires high human effort in data structuring, while GenAI requires high effort in prompt engineering and grounding.
NEW QUESTION # 11
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?
Answer: B
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 # 12
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: A
NEW QUESTION # 13
Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?
Answer: C
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 # 14
What BEST protects sensitive test data at rest and in transit?
Answer: C
Explanation:
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.
NEW QUESTION # 15
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