2025年最新のGenerative-AI-Leader試験解答最新版PassTest Generative-AI-LeaderのPDF問題集をダウンロードせよ（47問題と解答)
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質問 # 15
A software developer needs a highly efficient, open-source large language model that can be fine-tuned on a local machine for rapid prototyping of a chatbot application. They require a model that offers strong performance in natural language understanding and generation, while being lightweight enough to run on limited hardware. Which Google-developed family of models should they use?
- A. Imagen
- B. Veo
- C. Gemini
- D. Gemma
正解：D
解説：
Gemma is Google's family of lightweight, state-of-the-art open models, built from the same research and technology used to create the Gemini3 models. They are designed for developers to build innovative AI applications on their local machines or in the cloud, offering a balance of performance and efficiency suitable for limited hardware and rapid prototyping. Veo is for video generation, Gemini is typically larger and more general-purpose, and Imagen is for image generation.
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質問 # 16
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?
- A. Code agent
- B. Customer service agent
- C. Data agent
- D. Security agent
正解：D
解説：
Given the tasks involve researching threats and creating detection rules, the most appropriate and specialized agent would be a Security agent. This type of agent would be pre-configured or easily adaptable to understand security-specific contexts, data, and actions within a CISO's domain.
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質問 # 17
A large e-commerce company with a substantial product catalog and many support documents has customers struggling to find information on their website. This leads to high support costs and poor user experience. The company wants a Google Cloud solution to improve website search and reduce support costs while improving customer satisfaction. What Google Cloud product should the company use?
- A. Vertex AI Platform
- B. Vertex AI Search
- C. Google Search
- D. Google Shopping
正解：B
解説：
Vertex AI Search is ideal for this scenario. It allows companies to build sophisticated search experiences over their own product catalogs and support documents. This improves accuracy and helps customers find what they need, directly addressing high support costs and poor user experience. Vertex AI Platform is broader for general ML development, Google Shopping is for consumers, and Google Search is for the public web.
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質問 # 18
A large e-commerce company with a vast and frequently updated product catalog finds that customers struggle to find products on their website, and support agents spend too much time finding detailed product information. The company wants to improve search accuracy and efficiency for both customers and support.
What Google Cloud solution should they use?
- A. Pre-built RAG with Vertex AI Search
- B. Vertex AI Natural Language API
- C. Vertex AI Conversation
- D. Vertex AI Model Garden
正解：A
解説：
This scenario strongly points to the need for accurate and up-to-date information retrieval from a product catalog. Pre-built RAG (Retrieval-Augmented Generation) combined with Vertex AI Search is the ideal solution. Vertex AI Search can index the product catalog, and RAG can then use thisindexed data to ground the responses of a generative AI model, ensuring that both customer searches and support agent queries retrieve precise and relevant product information.
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質問 # 19
A financial institution uses generative AI (gen AI) to approve and reject loan applications, but gives no reasons for rejection. Customers are starting to file complaints. The company needs to implement a solution to reduce the complaints. What should the company do?
- A. Implement explainable gen AI policies.
- B. Fine-tune the gen AI model.
- C. Collect a larger and more diverse dataset for the gen AI model.
- D. Develop fairness assessments for the gen AI model.
正解：A
解説：
The core problem is the lack of reasons for rejection, leading to customer complaints. This falls under the domain of explainable AI (XAI). Implementing explainable gen AI policies or mechanisms would allow the institution to provide transparency into how the AI made its decision, addressing the customer complaints directly. While other options might improve the model, they don't directly solve the transparency issue.
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質問 # 20
A logistics company wants to use a generative AI (gen AI) agent to automatically check real-time inventory levels across its warehouses and adjust delivery schedules. The gen AI agent needs access to internal inventory data. They want the most cost-effective solution. What should the organization do?
- A. Use Google Cloud databases and Vertex AI for the agent to get live data.
- B. Use Vertex AI Studio to fine-tune a model with sample inventory data.
- C. Use pre-built gen AI chatbots for inventory questions.
- D. Build a custom API instead of using the gen AI agent.
正解：A
解説：
To achieve real-time inventory checks and adjust delivery schedules, the generative AI agent needs live access to the company's internal inventory data. Google Cloud databases provide the structured storage for this data, and Vertex AI offers the platform to build, deploy, and manage the AI agent, including connecting it to these live data sources. This approach allows the agent to make informed decisions based on current information. Building a custom API for every interaction might be less cost-effective in the long run for dynamic inventory data. Pre-built chatbots might not have the direct integration needed for real-time adjustments, and fine-tuning with sample data wouldn't provide the live data access required.
質問 # 21
A development team is building an internal knowledge base chatbot to answer employee questions about company policies and procedures. This information is stored across various documents in Google Cloud Storage and is updated regularly by different departments. What is the primary benefit of using Google Cloud's RAG APIs in this scenario?
- A. They enable the generative AI model to retrieve the most up-to-date and relevant information from the policy documents in real-time.
- B. They provide a pre-built user interface for the chatbot, simplifying the front-end development process.
- C. They automatically create summaries of all company policies, which are then presented to employees as quick answers.
- D. They allow the development team to train a single foundation model on all company documents.
正解：A
解説：
The primary benefit of RAG (Retrieval-Augmented Generation) in this context is its ability to ensure the chatbot provides accurate and up-to-date information. By retrieving relevant and recent policy documents from Cloud Storage in real-time and then grounding the LLM's response with this information, the chatbot avoids hallucinating or providing outdated answers, which is crucial for an internal knowledge base.
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質問 # 22
What will Google Cloud's Agent Assist help a company achieve?
- A. The infrastructure to provide an enterprise-grade contact center solution with omnichannel support, routing, and integration with CRM systems.
- B. The ability to analyze conversational data to identify customer sentiment, common topics of discussion, and insights into agent performance and customer experience.
- C. The ability to provide real-time assistance and recommended responses to live customer service agents during their interactions.
- D. The ability to build and deploy deterministic and generative chatbot agents for automated customer support.
正解：C
解説：
Google Cloud's Agent Assist is specifically designed to augment human customer service agents. It provides real-time suggestions, retrieves relevant information, and offers recommended responses to agents during live interactions, improving their efficiency and consistency.
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質問 # 23
An organization needs an AI tool to analyze and summarize lengthy customer feedback text transcripts. You need to choose a Google foundation model with a large context window. What foundation model should the organization choose?
- A. Chirp
- B. Gemini
- C. Imagen
- D. CodeGemma
正解：B
解説：
Gemini models are known for their large context windows, making them highly suitable for processing and summarizing lengthy texts like customer feedback transcripts. CodeGemma is specialized for code, Imagen for image generation, and Chirp for speech.
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質問 # 24
A highly regulated financial institution wants to use Gemini as the core decision engine for a loan approval system that will deterministically approve or reject loan applications based on a strict set of predefined criteria. Why is this an inappropriate use case for Gemini?
- A. Gemini deployment for this scenario would be too expensive and complex.
- B. Gemini cannot integrate with required financial databases.
- C. Gemini is designed for flexible content generation and inference, not rigid rule-based decisions.
- D. Gemini is not equipped to handle structured numerical data for financial assessments.
正解：C
解説：
Gemini, as a large language model, excels at flexible content generation, summarization, understanding, and inference. However, it is not designed for deterministic, rule-based decision-making that requires absolute consistency and adherence to strict, predefined criteria, as is common in highly regulated financial systems like loan approvals. Such systems typically require traditional programming logic or specific rule engines for auditable and consistent outcomes.
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質問 # 25
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively. What machine learning should the company use?
- A. Supervised learning
- B. Deep learning
- C. Unsupervised learning
- D. Reinforcement learning
正解：D
解説：
This scenario perfectly describes reinforcement learning. In reinforcement learning, an agent learns to make decisions by interacting with an environment, receiving1 rewards for desirable actions and penalties for undesirable ones,2 and iteratively improving its behavior through trial and error to maximize cumulative reward.
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質問 # 26
A company wants to use an AI agent to automate some tasks. They want everyone to understand the different functions of an AI agent. What is the function of an AI agent in the context of gen AI?
- A. To provide the computational resources needed to train and run gen AI models.
- B. To provide a user-friendly interface for interacting with gen AI models.
- C. To store and manage large datasets used for training and running gen AI models.
- D. To analyze situations, use multiple tools, and make informed decisions without requiring constant human input.
正解：D
解説：
An AI agent, especially in the context of generative AI, is designed to be more autonomous and capable than a simple model. Its function is to understand a goal, analyze a situation, leverage various tools (including other generative AI models or external APIs), and make decisions or take actions to achieve that goal, often with minimal human intervention.
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質問 # 27
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?
- A. Code agent
- B. Customer service agent
- C. Data agent
- D. Security agent
正解：D
解説：
Given the tasks
質問 # 28
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?
- A. Use Gemini for Google Workspace to facilitate collaborative document review.
- B. Use Vertex AI Agent Builder to create a custom AI agent.
- C. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
- D. Use Vertex AI Search to index the papers and enable keyword-based searches.
正解：B
解説：
The requirement is to answer questions about the documents and provide summarized insights without requiring extensive coding expertise. Vertex AI Agent Builder is designed precisely for creating custom AI agents, often with low-code or no-code capabilities, that can interact with and process large volumes of information like scientific papers. While Vertex AI Search could index papers for keyword searches, it doesn't directly answer questions or provide summarized insights in the same way a generative AI agent built with Agent Builder could. Gemini for Google Workspace is for collaborative work, not specifically for building custom AI agents for document analysis. Vertex AI AutoML is for training classification models, which is different from answering questions and summarizing.
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質問 # 29
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?
- A. The frequency of updates to the underlying foundation models used by different gen AI platforms.
- B. The number of employees who will be trained to use the new gen AI tools.
- C. The specific business problems the company aims to solve and the desired outcomes.
- D. The availability of pre-trained models that are offered on various cloud computing platforms.
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 # 30
A large multinational corporation with geographically dispersed teams struggles with knowledge silos and inconsistent access to crucial internal information. What is a key business benefit of using Google Agentspace in this scenario?
- A. Automation of employee performance reviews using AI.
- B. Improved IT infrastructure management across offices.
- C. Enhanced data encryption and compliance for internal communications.
- D. Seamless knowledge sharing and collaboration across internal systems.
正解：D
解説：
Google Agentspace (or similar agent-based frameworks) aims to connect and orchestrate various AI capabilities and data sources. In a scenario with knowledge silos, a key benefit would be to enable seamless knowledge sharing and collaboration by allowing agents to access, process, and disseminate information across different internal systems and teams.
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質問 # 31
A development team is configuring a generative AI model for a customer-facing application and wants to ensure the generated content is appropriate and harmless. What is the primary function of the safety settings parameter in a generative AI model?
- A. To limit the maximum text length that the model generates by ensuring concise responses.
- B. To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.
- C. To control the creativity and randomness of the model's output by adjusting the diversity of word choices.
- D. To filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.
正解：D
解説：
Safety settings in generative AI models are specifically designed to prevent the generation of content that could be harmful, offensive, or inappropriate. This includes filtering for categories like hate speech, sexually explicit content, self-harm, and violence, based on predefined thresholds. Options A, B, and D refer to other parameters like max_output_tokens or temperature, which control output length, input/output processing, and creativity, respectively, not safety.
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質問 # 32
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Generative-AI-Leader試験解答問題集：https://www.passtest.jp/Google/Generative-AI-Leader-shiken.html（47問題と解答)
無料2025年最新のGoogle Cloud Certified Generative-AI-Leader問題集を提供しております！PassTest：https://drive.google.com/open?id=1SzZ_kpdZdLDUzU5AfcD0ElsLocqriIMj