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>> Exam Generative-AI-Leader Lab Questions <<
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NEW QUESTION # 41
A user asks a generative AI model about the scientific accuracy of a popular science fiction movie. The model confidently states that humans can indeed travel faster than light, referencing specific but entirely fictional theories and providing made-up explanations of how this is achieved according to the movie's "established science." The model presents this information as factual, without indicating that it originates from a fictional work. What type of model limitation is this?
Answer: A
Explanation:
The limitation described is the AI model generating a false or misleading response (humans traveling faster than light is scientifically impossible/unproven) and presenting it as fact (confidently stating a fictional theory is real) without the ability to indicate its uncertainty or the source's fictional nature. This is the definition of a Hallucination in generative AI. AI Hallucinations occur when a Large Language Model (LLM) generates outputs that are factually incorrect, irrelevant, or nonsensical, despite being linguistically fluent and seemingly plausible. They arise because the model is designed to predict the most statistically probable next word or token based on its training data, even when it lacks information or when its training data contains a mixture of fact and fiction. The model is overconfident in its generated response, a behavior that diminishes user trust and reliability, especially in applications where factual accuracy is critical.
NEW QUESTION # 42
A generative AI assistant at a mid-size logistics firm is asked to create a multi-city delivery itinerary. It collects initial constraints and preferences, drafts a tentative route, asks for clarifications or queries a tool for external data, updates the plan with the new information, and repeats these steps until the objective is satisfied or a limit of eight iterations is reached. This recurring cycle of observing context, reasoning internally, deciding on the next step, and acting until a goal or constraint is met is a defining characteristic of which component in an AI agent?
Answer: B
Explanation:
The scenario describes a repeated cycle of observing context, thinking, choosing the next action, performing that action such as calling a tool or asking for clarification, then incorporating the result and continuing until a goal or a limit is reached. This is exactly what the agent's control loop does. It governs how the agent plans across turns, manages tool use, updates working state, and stops when a success condition or an iteration cap such as eight steps is met.
NEW QUESTION # 43
A financial services company receives a high volume of loan applications daily submitted as scanned documents and PDFs with varying layouts. The manual process of extracting key information is time- consuming and prone to errors. This causes delays in loan processing and impacts customer satisfaction. The company wants to automate the extraction of this critical data to improve efficiency and accuracy. Which Google Cloud tool should they use?
Answer: C
Explanation:
Document AI API is specifically designed for intelligent document processing. It uses machine learning to extract structured data from unstructured documents like scanned forms and PDFs, even with varying layouts.
This directly addresses the challenge of automating data extraction from loan applications. Natural Language API focuses on text understanding, Vision AI on image analysis (not structured extraction from documents), and Dataflow is for data processing pipelines.
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NEW QUESTION # 44
A language learning startup called VerbaQuest wants to improve outcomes for its learners.
Rather than a fixed syllabus, its app will use generative AI to observe each learner's quiz results in real time. When a learner has trouble with a grammar rule, the app immediately produces a simpler explanation and proposes a 5-question targeted drill. When the learner shows mastery, the app advances them to more challenging lessons and exercises. Which generative AI use case does this most closely reflect?
Answer: D
Explanation:
This scenario describes an app that continuously tailors explanations and practice to each learner based on real time quiz performance. It simplifies instruction when a learner struggles and advances them when they demonstrate mastery. That is the essence of adaptivity and personalization because the system shapes the pace, difficulty, and content for each individual rather than following a fixed syllabus.
Generative AI is the mechanism that produces the customized explanations and targeted drills, yet the defining pattern is the closed loop of observing performance, deciding on the next best action for this learner, and delivering bespoke content. That full loop is what characterizes an adaptive and personalized learning experience.
NEW QUESTION # 45
According to Google-recommended practices, when should generative AI be used to automate tasks?
Answer: D
Explanation:
The strategic value of Generative AI (Gen AI) in a business context, as taught in Google's courses, is primarily to enhance efficiency and productivity by taking over tasks that consume significant employee time.
Gen AI excels in automating tasks that:
Are repetitive and time-consuming, such as drafting initial emails, summarizing long documents, or generating code snippets. Automating these routine tasks (C) frees employees to focus on higher-value activities (like building customer relationships or strategic planning). Involve the generation of new content based on patterns learned from large datasets (e.g., text, images, code).
NEW QUESTION # 46
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