更新されたのは2026年02月22日認定試験Generative-AI-Leader問題集で練習テスト問題
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質問 # 25
A company wants to create an AI-powered educational solution that provides personalized learning experiences for students. This platform will assess a student's knowledge, recommend relevant learning materials, and generate personalized exercises. The application would provide the structure for lessons and track progress. What type of AI solution should they use?
- A. A learning management system (LMS)
- B. A customized learning agent
- C. A large language model fine-tuned on educational content
- D. An AI-powered recommendation system for learning resources
正解：B
解説：
The request goes beyond just recommendations or content generation. It involves assessing knowledge, recommending materials, generating personalized exercises, providing lesson structure, and tracking progress. This implies a more comprehensive, intelligent system that acts as an assistant or tutor for the student, which is best described as a customized learning agent. This agent would likely leverage LLMs and recommendation systems as components, but the overall solution is an agent.
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質問 # 26
A company wants to use generative AI to create a chatbot that can answer customer questions about their products and services. They need to ensure that the chatbot only uses information from the company's official documentation. What should the company do?
- A. Use prompt chaining.
- B. Use grounding.
- C. Adjust the temperature parameter.
- D. Use role prompting.
正解：B
解説：
Grounding is the technique of "grounding" the LLM's responses in specific, authoritative data sources (like the company's official documentation). This prevents the model from "hallucinating" or providing information outside of the approved knowledge base, ensuring accuracy and relevance to the company's specific products and services.
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質問 # 27
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 number of employees who will be trained to use the new gen AI tools.
- B. The availability of pre-trained models that are offered on various cloud computing platforms.
- C. The specific business problems the company aims to solve and the desired outcomes.
- D. The frequency of updates to the underlying foundation models used by different gen AI 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 # 28
A security team needs a centralized platform to gain a comprehensive overview of their organization's security health across their entire Google Cloud environment, including potential threats to their generative AI deployments. Which Google Cloud security offering is specifically for this purpose?
- A. Secure-by-design infrastructure
- B. Identity and Access Management
- C. Security Command Center
- D. Workload monitoring tools
正解：C
解説：
Security Command Center is Google Cloud's comprehensive security management and data risk platform. It provides centralized visibility into security posture, identifies vulnerabilities, detects threats, and helps manage compliance across the entire Google Cloud environment, includingservices and deployments like generative AI.
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質問 # 29
A learning and development team wants to quickly create a new hire training video with a custom avatar and voiceover that matches their company's branding and key messaging. They did not receive any money to spend on the production. What should they do?
- A. Create a video with Google Vids.
- B. Train a model with Vertex AI and produce a video.
- C. Prompt the Gemini app to create a video.
- D. Generate the video frames with Imagen.
正解：A
解説：
The scenario requires quick creation of a training video using a custom avatar and voiceover while adhering to zero cost for production.
Google Vids is an AI-powered video creation app (part of Google Workspace/Gemini features) designed to make video creation accessible for teams without the overhead of traditional production. It specifically offers features like AI avatars and voiceovers for content such as trainings, demos, and onboarding videos. This directly addresses the need for a low-cost, fast solution for a new hire training video with custom branding elements (custom avatars and voiceovers are a key feature of the tool).
Option A, Imagen, is a Google foundation model specialized for image generation, not the creation of structured, narrated training videos with avatars. Option B, using the Gemini app, is primarily for text, code, and multimodal chat/generation, and is not the dedicated Google application for video production. Option C, training a model with Vertex AI, is a highly technical, time-consuming, and expensive endeavor that violates the need for a quick and zero-cost solution. Therefore, using the purpose-built, gen AI-enabled Google Vids application is the correct and most efficient choice.
質問 # 30
A company wants to create an AI-powered educational solution that provides personalized learning experiences for students. This platform will assess a student's knowledge, recommend relevant learning materials, and generate personalized exercises. The application would provide the structure for lessons and track progress. What type of AI solution should they use?
- A. A learning management system (LMS)
- B. A customized learning agent
- C. A large language model fine-tuned on educational content
- D. An AI-powered recommendation system for learning resources
正解：B
解説：
The request goes beyond just recommendations or content generation. It involves assessing knowledge, recommending materials, generating personalized exercises, providing lesson structure, and tracking progress.
This implies a more comprehensive, intelligent system that acts as an assistant or tutor for the student, which is best described as a customized learning agent. This agent would likely leverage LLMs and recommendation systems as components, but the overall solution is an agent.
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質問 # 31
An organization wants to use generative AI to create a marketing campaign. They need to ensure that the AI model generates text that is appropriate for the target audience. What should the organization do?
- A. Use few-shot prompting.
- B. Use role prompting.
- C. Use prompt chaining.
- D. Adjust the temperature parameter.
正解：B
解説：
Role prompting is a technique where you instruct the generative AI model to "act as" a specific persona or character. By assigning the model a role (e.g., "Act as a marketing expert writing for a young, tech-savvy audience"), you can guide its tone, style, and content to be appropriate for the target audience of the marketing campaign.
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質問 # 32
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?
- A. One-shot prompting that provides a single example of a good customer service email.
- B. Role prompting that instructs the AI to act as an experienced customer service representative with corporate knowledge.
- C. Few-shot prompting that provides examples of good and bad customer service emails.
- D. Prompt chaining that engages the AI in a conversation to gather the necessary information before generating the email response.
正解：B
解説：
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.)
質問 # 33
An organization is collecting data to train a generative AI model for customer service. They want to ensure security throughout the ML lifecycle. What is a critical consideration at this stage?
- A. Applying the latest software patches to the AI model on a regular basis.
- B. Establishing ethical guidelines for AI model responses to ensure fairness and avoid harm.
- C. Implementing access controls and protecting sensitive information within the training data.
- D. Monitoring the AI model's performance for unexpected outputs and potential errors.
正解：C
解説：
The stage mentioned is Data Collection/Training Data Preparation. In the machine learning lifecycle, this initial stage is where raw data is ingested and processed. If the model is being trained for customer service, the data (e.g., customer transcripts) is highly likely to contain sensitive information (like Personally Identifiable Information or PII).
Therefore, the most critical security and privacy consideration at this stage is protecting the integrity and confidentiality of the data itself.
Implementing strong access controls and protecting sensitive information (A) is the essential first step in a secure AI pipeline, aligning with Google's Secure AI Framework (SAIF). If data access is not controlled and sensitive data is not de-identified or redacted before it is used for training, the resulting model could leak that sensitive information to users.
Options B, C, and D are all important controls, but they occur at later stages of the ML lifecycle:
B (Software patches/latest versions) is part of deployment and management.
C (Ethical guidelines/fairness) is a Responsible AI goal implemented via guardrails and testing (later stages).
D (Monitoring) is an MLOps step that happens after deployment.
The critical consideration at the data collection stage is ensuring the data's security and privacy before it influences the model.
(Reference: Google Cloud guidance on securing generative AI emphasizes that one of the most significant risks is data leakage, making safeguarding training data and implementing identity and access control the foundational steps in the data ingestion and preparation phases.)
質問 # 34
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 Vertex AI AutoML to train a model that classifies papers into predefined research areas.
- B. Use Vertex AI Agent Builder to create a custom AI agent.
- C. Use Vertex AI Search to index the papers and enable keyword-based searches.
- D. Use Gemini for Google Workspace to facilitate collaborative document review.
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 # 35
A company's large learning model (LLM) is producing hallucinations that are a result of the Knowledge cutoff. How does retrieval-augmented generation (RAG) overcome this limitation?
- A. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
- B. RAG fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.
- C. RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
- D. RAG uses human oversight to ensure accuracy before presenting information to the customer.
正解：C
解説：
The primary purpose of RAG is to address the "knowledge cutoff" and hallucination issues of LLMs. It does this by retrieving relevant, up-to-date information from external knowledge sources (like databases or documents) at inference time and then using this retrieved information to ground the LLM's generation, ensuring factual accuracy and relevance to the specific query.
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質問 # 36
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?
- A. Vision AI
- B. Natural Language API
- C. Dataflow
- D. Document AI API
正解：D
解説：
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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質問 # 37
What are core hardware components of the infrastructure layer in the generative AI landscape?
- A. Pre-trained models
- B. User interfaces
- C. TPUs and GPUs
- D. Tools and services for building AI models
正解：C
解説：
The Generative AI landscape is often broken down into several functional layers: Applications, Agents, Platforms, Models, and Infrastructure.
The Infrastructure Layer is the foundation, providing the physical and virtual computing resources necessary to run and train the large models. These resources include servers, storage, networking, and most importantly, the specialized hardware accelerators required for high-volume, parallel computation.
The core hardware components are the Graphics Processing Units (GPUs) and the custom-designed Tensor Processing Units (TPUs) (A). These accelerators are optimized for the massive matrix operations fundamental to deep learning and Gen AI model training and inference.
Options B (User interfaces) and D (Tools and services) refer to the Application and Platform layers, respectively.
Option C (Pre-trained models) refers to the Model layer.
The physical hardware underpinning these abstract layers are the TPUs and GPUs.
(Reference: Google Cloud Generative AI Study Guides state that the Infrastructure Layer provides the core computing resources needed for generative AI, including the physical hardware (like servers, GPUs, and TPUs) and the essential software needed to train, store, and run AI models.)
質問 # 38
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 filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.
- B. To limit the maximum text length that the model generates by ensuring concise responses.
- C. To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.
- D. To control the creativity and randomness of the model's output by adjusting the diversity of word choices.
正解：A
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 # 39
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. Gemini
- B. CodeGemma
- C. Chirp
- D. Imagen
正解：A
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 # 40
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 Search
- B. Google Search
- C. Vertex AI Platform
- D. Google Shopping
正解：A
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 # 41
A large company is creating their generative AI (gen AI) solution by using Google Cloud's offerings. They want to ensure that their mid-level managers contribute to a successful gen AI rollout by following Google-recommended practices. What should the mid-level managers do?
- A. Secure funding and resources for AI initiatives by demonstrating the potential return on investment to the chief financial officer (CFO).
- B. Drive gen AI adoption by identifying high-impact, feasible solutions that address specific challenges within their workflows.
- C. Create a robust data strategy to ensure teams can access high-quality, relevant data that is appropriate for training and fine-tuning gen AI models.
- D. Perform continuous testing, measurement, and refinement based on user feedback and real-world performance data.
正解：B
解説：
Google's recommended strategy for a successful generative AI rollout involves a combination of top-down strategic alignment and bottom-up adoption. In this structure, the role of the mid-level manager is critical for driving tangible value within their specific domain.
Securing funding (D) is typically the responsibility of senior leadership or the steering committee.
Creating a robust data strategy (B) is the domain of data governance teams and data scientists.
Continuous testing and refinement (A) is the job of MLOps/engineering teams and end-users.
The primary role of the mid-level manager is to act as the bridge between high-level strategy and daily operations. They possess the domain knowledge to pinpoint pain points. Therefore, their most impactful contribution is to identify specific, high-impact, and feasible use cases (C) for their teams-such as automating report summaries or drafting internal communications-that directly address operational challenges and demonstrate quick wins. This action fuels successful adoption and validates the AI strategy from the ground up.
(Reference: Google Cloud's guidance on Gen AI strategy emphasizes that successful adoption requires strong top-down vision (like defining goals/funding) combined with bottom-up discovery, where functional leaders (mid-level managers) identify and prioritize high-value, feasible solutions within their specific workflows to drive adoption.)
質問 # 42
A human resources team is implementing a new generative AI application to assist the department in screening a large volume of job applications. They want to ensure fairness and build trust with potential candidates. What should the team prioritize?
- A. Focusing on minimizing the processing time for each application to improve efficiency.
- B. Ensuring that the AI application can automatically rank all candidates without requiring human review.
- C. Ensuring AI operates transparently, especially regarding application evaluation and data usage.
- D. Integrating the AI application with various job boards to maximize candidate reach.
正解：C
解説：
To ensure fairness and build trust, especially in sensitive areas like job applications, transparency in how AI evaluates applications and uses data is paramount. This involves understanding potential biases, explaining decisions (where possible), and ensuring human oversight.
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質問 # 43
What is a primary benefit of using a multi-agent system?
- A. To simplify the most basic and repetitive rule-based tasks.
- B. To manage complex tasks that demand coordinated AI functions.
- C. To consolidate all unique AI functions into a single, undifferentiated model.
- D. To serve as a platform for hosting traditional, non-AI applications.
正解：B
解説：
Multi-agent systems are designed to tackle complex problems by breaking them down into sub-tasks, where each agent specializes in a specific function. These agents then coordinate and collaborate to achieve a larger, more intricate goal that a single, monolithic AI model might struggle with.
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質問 # 44
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