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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of Generative AI | 20% | - Generative AI Concepts
|
| Topic 2: Tools for Testing Generative AI | 20% | - Testing Tools Overview
|
| Topic 3: Testing Activities for Generative AI | 30% | - Requirements-Based Testing
|
| Topic 4: Risks and Testing Challenges for Generative AI | 30% | - Quality Risks Specific to Generative AI
|
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NEW QUESTION # 40
Which setting can reduce variability by narrowing the sampling distribution during inference?
Answer: D
Explanation:
In the context of LLM inference,Temperatureis a hyperparameter that controls the randomness or
"creativity" of the model's output. When the temperature is set high, the model's probability distribution is
"flattened," meaning it is more likely to select less-probable tokens, leading to more diverse and sometimes unpredictable text. For software testing, where precision and repeatability are paramount,lowering the temperature(Option C) is the standard practice. A temperature of 0.0 makes the model "deterministic," meaning it will consistently choose the token with the highest probability. This narrows the sampling distribution and significantly reduces variability between runs. While a larger context window (Option D) allows the model to process more information, it does not directly control the randomness of token selection.
Similarly, the "learning rate" (Option B) is a parameter used during thetrainingorfine-tuningphase, not during inference. For generating test cases or scripts that must follow strict logic, a lower temperature ensures that the model remains focused and produces consistent results.
NEW QUESTION # 41
A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?
Answer: C
Explanation:
A/B testing, also known as split testing, is a systematic empirical method used to compare two versions of a prompt (Version A and Version B) to determine which one performs better based on predefined evaluation metrics. In the realm of LLMs, where outputs can be stochastic (probabilistic), A/B testing is essential for mitigating inconsistency. When a team encounters vague or varying results for a user story, simply modifying the prompt iteratively (Option B) may improve the result but does not provide a statistical or objective basis for why one version is superior. Byrunning A/B tests, testers can evaluate prompts against specific KPIs such as accuracy, relevance, format adherence, or the absence of hallucinations. This process involves sending the same input data through both prompt versions multiple times and scoring the outputs. The version that consistently yields the "stronger wording" or more precise testware is then selected as the production standard. This data-driven approach is a cornerstone of prompt engineering in professional environments, ensuring that the most effective linguistic structures are utilized to maximize the model's performance and reliability.
NEW QUESTION # 42
Who typically defines the system prompt in a testing workflow?
Answer: B
Explanation:
In professional Generative AI applications, thesystem prompt(sometimes called the system message) is the foundational set of instructions that defines the AI's persona, boundaries, and overall behavior. In a testing workflow, this is typically defined by atester or test engineerwho is configuring the AI assistant for a specific project. Unlike the user prompt, which changes with every interaction, the system prompt remains relatively static and acts as a "guardrail" to ensure the model stays in its role (e.g., "You are an expert in ISO
26262 automotive testing standards"). By defining the system prompt, the tester ensures that the model consistently uses specific terminology, adheres to data privacy constraints, and formats its output according to the team's requirements. While end users (Option B) provide the task-specific input, they do not usually have the permissions or technical need to alter the underlying system-level instructions. Similarly, while CI servers (Option C) might trigger the prompt, they do not "define" the human-centric logic contained within it.
Properly crafting the system prompt is a core part of setting up an AI-augmented test environment.
NEW QUESTION # 43
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 # 44
What does an embedding represent in an LLM?
Answer: D
Explanation:
Embeddingsare a fundamental concept in modern Natural Language Processing (NLP) and LLMs. They are high-dimensional numerical vectors-essentially lists of numbers-that represent the meaning (semantics) of a piece of text (a word, sentence, or document). Unlike traditional keyword matching, which looks for identical strings of characters, embeddings allow the model to understand the "closeness" of concepts. For example, in a vector space, the word "bug" would be mathematically closer to "defect" or "error" than to
"feature" or "requirement." This captures the semantic relationship between terms. This technology is the backbone of Retrieval-Augmented Generation (RAG) used in testing: when a tester queries a documentation set, the system converts the query into an embedding and looks for other chunks of text with similar vector values. This allows the AI to retrieve relevant context even if the exact keywords do not match. It is not about logical rules (Option C) or groups of tokens (Option A), but rather a mathematical representation of language that enables machines to process human meaning.
NEW QUESTION # 45
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