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| Section | Objectives |
|---|---|
| Fundamentals of Generative AI | - Core concepts of generative AI and large language models - Key use cases and limitations of generative AI - Difference between traditional AI, machine learning, and generative AI |
| Responsible AI and Governance | - AI safety, bias, and fairness considerations - Data privacy and security in generative AI systems - Responsible AI principles and compliance |
| Business Applications and Adoption Strategy | - Identifying business use cases for generative AI - Measuring ROI and value of generative AI initiatives - AI-driven transformation and workflow integration |
| Google Cloud Generative AI Products and Tools | - AI APIs and model deployment options on Google Cloud - Vertex AI and Gemini models overview - Prompt design and prompt engineering tools |
>> Generative-AI-Leader考題資訊 <<
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問題 #94
An engineering team at example.com spends about 12 hours each week producing boilerplate and scaffolding for routine service endpoints, and they want an AI capability that can generate this repetitive code from concise requirements or from established patterns so the developers can concentrate on complex tasks. What primary use of generative AI does this scenario represent?
答案：B
解題說明：
The scenario describes generating boilerplate and scaffolding from concise requirements or established patterns so developers can focus on more complex work.
This use case aligns with generative AI that produces source code from prompts or patterns. It automates repetitive implementation details, accelerates service setup, and helps teams reduce time spent on routine endpoint creation while maintaining consistency.
問題 #95
A company wants to adopt generative AI and is concerned about vendor lock-in. They want to maintain flexibility in their technology stack. What Google Cloud strength would ease their concerns?
答案：B
解題說明：
Google Cloud promotes an open and flexible approach to its AI offerings, supporting open standards, open- source initiatives (like TensorFlow, Kubernetes, and Gemma), and providing various integration options. This helps alleviate vendor lock-in concerns by giving customers choice and control over their technology stack.
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問題 #96
A customer service team wants to use generative AI to improve the quality and consistency of their email responses to customer inquiries. They need a solution that can guide the AI to adopt a helpful, empathetic tone while adhering to company policies. Which prompting technique should they use?
答案：B
解題說明：
The most direct and effective way to influence the style, personality, and knowledge context of an AI's response is through Role Prompting.
Role Prompting involves instructing the model to assume a specific persona (a "role") before responding. By assigning the AI the role of an "experienced customer service representative" (B), the model is implicitly directed to adopt a professional, helpful, and empathetic tone. Furthermore, specifying "with corporate knowledge" directs the model to prioritize responses consistent with internal company policies. This technique is a foundational element of prompt engineering, often used in conjunction with other methods (like grounding, if specific policy documents were needed) to dramatically shift the output style and relevance.
While Few-shot prompting (D) could provide examples to influence style, it's less efficient than a clear role instruction and still requires the model to infer the persona. Prompt Chaining (A) is used to manage multi-turn conversation memory, not to set the tone or persona. Therefore, defining the Role is the core technique for establishing both the desired tone and the necessary professional context in a single instruction.
(Reference: Google's documentation on prompt engineering for customer service shows examples where users begin the prompt with "I am a customer service representative" to set the tone and persona for the generated response, confirming Role Prompting as the technique for ensuring style and consistency.)
問題 #97
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?
答案：D
解題說明：
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.
問題 #98
A company wants to choose a generative AI (gen AI) use case that will be successful and have the most impact. What key factor should they determine first according to Google Cloud-recommended practices?
答案：B
解題說明：
A fundamental principle for successful AI adoption, including generative AI, is to start with clear business problems and desired outcomes. Without a well-defined problem, the AI solution might not deliver meaningful value, regardless of the technology used. This "problem-first" approach is crucial for impactful AI strategy.
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問題 #99
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