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| Section | Objectives |
|---|---|
| Fundamentals of Generative AI | - Core concepts of generative AI and large language models - Difference between traditional AI, machine learning, and generative AI - Key use cases and limitations of generative AI |
| Business Applications and Adoption Strategy | - Measuring ROI and value of generative AI initiatives - Identifying business use cases for generative AI - 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 |
| Responsible AI and Governance | - AI safety, bias, and fairness considerations - Responsible AI principles and compliance - Data privacy and security in generative AI systems |
>> Generative-AI-Leader日本語試験情報 <<
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質問 # 102
An order fulfillment team has an agent that automatically processes orders, updates inventory, sends shipping notifications, and handles returns. What type of agent is this?
正解：A
解説：
Generative AI agents are typically categorized based on the goal they are designed to achieve.
The agent described is performing a sequence of distinct, interconnected, operational tasks (processes orders, updates inventory, sends notifications, handles returns). These steps are typical components of a business workflow or process automation.
A Workflow Agent is an AI agent whose purpose is to automate and manage an entire business process or a complex multi-step sequence of operations that traditionally required manual handoffs between different systems or teams. It uses its large language model brain, coupled with tools (such as APIs to a CRM, Inventory database, or shipping system), to observe the state of a process (e.g., a new order), reason about the next step, and execute the necessary actions to move the process forward toward completion.
Customer Service Agents (C) and Conversational Agents (D) are focused on user interaction (chatbots, virtual assistants) rather than back-end transactional automation.
Employee Productivity Agents (B) typically focus on individual tasks like drafting emails, summarizing meetings, or internal search, not automating an end-to-end operational flow like order fulfillment.
Therefore, an agent designed to automate a complete, multi-step business process like order fulfillment is correctly classified as a Workflow Agent.
(Reference: Google Cloud Generative AI training materials categorize agents based on function, with Workflow Agents being those designed to automate multi-step business processes and operational sequences.)
質問 # 103
A data analyst at MetroVoyage tests a foundation model by writing the prompt "Translate the phrase 'good evening' into Italian." The instruction is given directly and no example translations are included. Which prompting technique is being applied?
正解：A
解説：
Since the model is asked to translate using only an instruction and no example translations are provided.
In Zero-shot prompting the model relies on its pretraining to perform the task without any demonstrations. The prompt directly states the task which is to translate the phrase 'good evening' into Italian and it contains no sample input output pairs which matches this technique.
質問 # 104
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?
正解：A
解説：
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.
質問 # 105
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?
正解：C
解説：
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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質問 # 106
What does a diffusion model do?
正解：C
解説：
A Diffusion Model (or Denoising Diffusion Probabilistic Model) is a specific class of generative AI model that is best known for its ability to create highly realistic images (e.g., Google's Imagen and Stable Diffusion are based on this architecture).
The core mechanism of a diffusion model is a two-step process:
Forward Diffusion (Adding Noise): It learns how to gradually corrupt data (like an image) by adding random noise until the original content is completely indistinguishable. Reverse Diffusion (Denoising): It then learns to reverse this process--to gradually remove the noise--starting from a random noise pattern and iteratively refining it, guided by a text prompt, until a clear, coherent, and high-quality piece of content (an image or video) is generated. Option D accurately captures this mechanism: the model starts with pure noise and generates the final structured data (the image) by refining that noise.
質問 # 107
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