2026年最新の実際に出る無料Google Generative-AI-Leader試験問題集と解答
Generative-AI-Leader練習テストエンジンで今すぐ試そう76試験問題
Google Generative-AI-Leader 認定試験の出題範囲：
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質問 # 30
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. Identity and Access Management
- B. Workload monitoring tools
- C. Security Command Center
- D. Secure-by-design infrastructure
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 # 31
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 serve as a platform for hosting traditional, non-AI applications.
- D. To consolidate all unique AI functions into a single, undifferentiated model.
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 # 32
A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone.
What type of machine learning should they use?
- A. Unsupervised learning
- B. Supervised learning
- C. Reinforcement learning
- D. Deep learning
正解：A
解説：
Since the team has not yet assigned any labels or categories to the sensor readings and wants to identify
"anomalies, malfunctions, or natural groupings" based on the data alone, this is a classic unsupervised learning problem. Unsupervised learning techniques like clustering or anomaly detection are used to find hidden patterns or structures in unlabeled data.
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質問 # 33
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?
- A. Google Cloud's strict adherence to proprietary technologies ensures the highest level of security and performance.
- B. Google Cloud's AI solutions have an open approach that supports customer choice across offerings.
- C. Google Cloud's AI solutions are pre-packaged for easy deployment, eliminating the need for customization and integration efforts.
- D. Google Cloud's focus on automation aims to replace human jobs with AI systems, potentially leading to significant workforce reductions.
正解：B
解説：
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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質問 # 34
An organization wants to quickly experiment with different Gemini models and parameters for content creation without a complex setup. What service should the organization use for this initial exploration?
- A. Vertex AI Studio
- B. Vertex AI Prediction
- C. Gemini for Google Workspace
- D. Google AI Studio
正解：A
解説：
The requirement is for a tool that facilitates quick experimentation with Gemini models and parameters without requiring significant technical setup, specifically targeting content creation (prompting/tuning) within the enterprise environment.
Vertex AI Studio (C) is the low-code, web-based UI component of Google Cloud's unified ML platform (Vertex AI). It is explicitly designed for non-technical users, developers, and data scientists to:
Quickly prototype and test different Foundation Models (including Gemini, Imagen, and Codey).
Experiment with model parameters (like Temperature, Top-P, and Max Output Tokens) through a user-friendly interface.
Refine prompts and set up initial tuning or grounding configurations before moving to large-scale production deployment.
Google AI Studio (A) is a very similar tool, but it's generally associated with non-enterprise/public prototyping for Google's models, whereas Vertex AI Studio is the enterprise-ready environment for Gen AI development on Google Cloud, which is the context of the exam.
Vertex AI Prediction (B) is the service for deploying and serving models for inference, not for initial experimentation.
Gemini for Google Workspace (D) is an application that uses Gen AI to boost productivity within apps like Docs and Gmail, but it does not provide the interface needed to experiment with models and tune parameters.
(Reference: Google Cloud documentation positions Vertex AI Studio as the low-code/no-code interface for rapidly prototyping, testing, and customizing Google's Foundation Models (like Gemini) before full production deployment.)
質問 # 35
A marketing team wants to use a generative AI model to create product descriptions for their new line of eco-friendly water bottles. They provide a brief prompt stating, "Write a product description for our new water bottle." The model generates a generic, lackluster description that is factually accurate but lacks engaging language and doesn't highlight the environmental benefits that are key to their brand. What should the marketing team do to overcome this limitation of the generated product description?
- A. Add details to the prompt about the audience, tone, and keywords.
- B. Lower the temperature setting of the model to produce more consistent results.
- C. Train the model on a dataset of marketing materials from other eco-friendly brands.
- D. Increase the token count for the model to allow for longer descriptions.
正解：A
解説：
The core problem described is a lackluster and generic output that fails to capture the desired tone and key information (environmental benefits). This is a classic limitation of zero-shot prompting (a brief, un-detailed prompt), where the generative AI model relies solely on its general training data and lacks the necessary context to produce a highly relevant and engaging response. The solution is to improve the quality of the prompt itself, a process known as Prompt Engineering.
Option A, training the model, is an expensive and time-consuming process (fine-tuning) that is usually unnecessary for stylistic or content-specific guidance that can be achieved with a good prompt. Options C and D control the length and creativity, respectively, but don't inject the missing information or brand requirements.
Adding details to the prompt is the most immediate and effective technique to guide the model. By specifying the target audience (e.g., eco-conscious consumers), the desired tone (e.g., enthusiastic, persuasive), and mandatory keywords (e.g., "sustainable," "BPA-free," "ocean-friendly"), the marketing team is effectively providing the model with the necessary constraints and context to produce a description that is tailored to their brand and marketing goals. This technique is fundamental to improving the output of generative AI models without resorting to model customization.
質問 # 36
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. Adjust the temperature parameter.
- B. Use prompt chaining.
- C. Use few-shot prompting.
- D. Use role prompting.
正解：D
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 # 37
A social media platform uses a generative AI model to automatically generate summaries of user-submitted posts to provide quick overviews for other users. While the summaries are generally accurate for factual posts, the model occasionally misinterprets sarcasm, satire, or nuanced opinions, leading to summaries that misrepresent the original intent and potentially cause misunderstandings or offense among users. What should the platform do to overcome this limitation of the AI-generated summaries?
- A. Decrease the output length of the summaries to make them more concise.
- B. Increase the temperature parameter of the model to encourage more varied and less literal interpretations.
- C. Incorporate a human-in-the-loop (HITL) review process to refine the summaries.
- D. Implement stricter safety settings to filter out potentially misinterpreted content altogether.
正解：C
解説：
When AI struggles with nuances like sarcasm or satire, human oversight is often the most effective solution.
A human-in-the-loop (HITL) process allows human reviewers to check, correct, and refine AI-generated content before it is published, ensuring accuracy and appropriateness, especially for sensitive or complex language.
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質問 # 38
A data science team needs a centralized and organized location to store its various model versions, track their metadata, and easily deploy them to the respective applications. What Google Cloud service should they use?
- A. Cloud Storage
- B. BigQuery
- C. Vertex AI Pipelines
- D. Model Registry
正解：D
解説：
A Model Registry (specifically part of Vertex AI Model Registry) is designed precisely for managing the lifecycle of machine learning models. It provides a centralized repository for storing, versioning, tracking metadata, and facilitating the deployment of models, which is essential for MLOps. Cloud Storage is for raw data, BigQuery for data warehousing, and Vertex AI Pipelines for workflow orchestration.
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質問 # 39
A sales manager wants to responsibly use generative AI (gen AI) to increase efficiency with their existing tasks. They want to allow the sales team to focus on building customer relationships and closing deals. How should the sales team use gen AI?
- A. To automate creative content like blog posts and social media updates to attract new leads.
- B. To analyze customer interactions on social media and automatically generate sales pitches tailored to their public profiles.
- C. To draft emails and provide real-time insights about customer needs.
- D. To replace the sales team's CRM system with a more intuitive and user-friendly interface.
正解：C
解説：
The strategic goal is to boost sales efficiency by shifting the team's focus to high-value activities (relationships and closing deals) by automating repetitive administrative tasks.
Option C directly addresses this goal by leveraging Gen AI's core capabilities for text generation and summarization/analysis:
Drafting emails automates a major time sink for sales reps (a common, repetitive task).
Providing real-time insights automates the labor-intensive research and manual data analysis required to understand customer needs, giving the rep instant, actionable context.
Options A and D are less direct solutions for improving sales efficiency: Option A is an expensive, high-risk platform replacement, not an efficiency use case. Option D describes marketing tasks, which, while related, are not the primary, day-to-day tasks that sales reps perform to clear their schedules for relationship building. Therefore, Gen AI's most effective role in sales is as a productivity assistant for drafting and quick research.
(Reference: Google Cloud documentation on sales enablement use cases emphasizes that Gen AI's role is to automate administrative and time-consuming tasks like drafting outreach messages and synthesizing customer information to enhance seller productivity, allowing them to focus on revenue-generating activities.)
質問 # 40
A company is trying to decide which platform to use to optimize its generative AI (gen AI) solutions. Why should the company use Vertex AI Platform?
- A. It provides a mechanism for efficient analysis and exploration of large datasets used in machine learning.
- B. It provides scalable and cost-effective object storage for data used in machine learning workflows.
- C. It provides a unified platform of tools for building, deploying, and managing machine learning.
- D. It provides gen AI coding assistance with enterprise security and privacy protection.
正解：C
解説：
Vertex AI is Google Cloud's core, end-to-end Machine Learning Operations (MLOps) platform, designed to cover the entire ML lifecycle.
The key benefit of Vertex AI, particularly for generative AI, is that it provides a unified platform (D) where all stages of AI development-from accessing foundation models in Model Garden, testing in Vertex AI Studio, training and tuning (via tools like Reinforcement Learning from Human Feedback), to deploying, and monitoring models in production-can be managed from a single service. This significantly reduces complexity, improves collaboration between teams (data scientists, engineers, business leaders), and ensures enterprise-grade governance and scalability necessary for production Gen AI solutions.
Option A describes BigQuery.
Option B describes Gemini Code Assist.
Option C describes Cloud Storage.
Vertex AI is the overarching platform that integrates all these tools to deliver a streamlined MLOps workflow.
(Reference: Google Cloud documentation states that Vertex AI is the unified AI development platform that brings together Google Cloud services for building, deploying, and managing machine learning models and generative AI solutions.)
質問 # 41
An organization wants granular control over who can use and see their generative AI models and related resources on Google Cloud. Which Google Cloud security offering is specifically for this purpose?
- A. Security Command Center
- B. Identity and Access Management
- C. Workload monitoring tools
- D. Secure-by-design infrastructure
正解：B
解説：
Identity and Access Management (IAM) is the fundamental Google Cloud service that allows you to define who has what access to which resources. It provides granular control over permissions for users, groups, and service accounts, including access to generative AI models and related data.
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質問 # 42
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. CodeGemma
- B. Chirp
- C. Gemini
- D. Imagen
正解：C
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 # 43
A company wants a generative AI platform that provides the infrastructure, tools, and pre-trained models needed to build, deploy, and manage its generative AI solutions. Which Google Cloud offering should the company use?
- A. Google Cloud Storage
- B. BigQuery
- C. Google Kubernetes Engine (GKE)
- D. Vertex AI
正解：D
解説：
Vertex AI is Google Cloud's unified machine learning platform that provides end-to-end support for the ML lifecycle, including access to pre-trained models (foundation models), tools for fine-tuning, deployment, and management of generative AI solutions. BigQuery is a data warehouse, GKE is for container orchestration, and Cloud Storage is for object storage; while they might be components used with Vertex AI, they are not the comprehensive generative AI platform themselves.
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質問 # 44
A marketing team wants to use a foundation model to create social media and advertising campaigns. They want to create written articles and images from text. They lack deep AI expertise and need a versatile solution. Which Google foundation model should they use?
- A. Gemini
- B. Imagen
- C. Gemma
- D. Veo
正解：A
解説：
Gemini is Google's most advanced and multimodal foundation model, capable of understanding and generating various forms of content, including text and images, from a single prompt. Its versatility makes it suitable for marketing teams that need to create diverse campaign materials without deep AI expertise. Imagen is specifically for image generation, Gemma is a family of smaller, open models, and Veo is for video generation.
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質問 # 45
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試験合格保証付きのGoogle Cloud Certified Generative-AI-Leader試験問題集：https://www.jpntest.com/shiken/Generative-AI-Leader-mondaishu
Google Generative-AI-Leader日常練習試験は2026年最新のに更新された76問あります：https://drive.google.com/open?id=1_R8JOeQI_RilRggin022CiMtSWxeucV3