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質問 # 38
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 Agent Builder to create a custom AI agent.
- B. Use Vertex AI Search to index the papers and enable keyword-based searches.
- C. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
- D. Use Gemini for Google Workspace to facilitate collaborative document review.
正解：A
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 # 39
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. Integrating the AI application with various job boards to maximize candidate reach.
- B. Focusing on minimizing the processing time for each application to improve efficiency.
- C. Ensuring that the AI application can automatically rank all candidates without requiring human review.
- D. Ensuring AI operates transparently, especially regarding application evaluation and data usage.
正解：D
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 # 40
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 Vertex AI Studio to fine-tune a model with sample inventory data.
- B. Use pre-built gen AI chatbots for inventory questions.
- C. Use Google Cloud databases and Vertex AI for the agent to get live data.
- D. Build a custom API instead of using the gen AI agent.
正解：C
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 # 41
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 draft emails and provide real-time insights about customer needs.
- B. To analyze customer interactions on social media and automatically generate sales pitches tailored to their public profiles.
- C. To automate creative content like blog posts and social media updates to attract new leads.
- D. To replace the sales team's CRM system with a more intuitive and user-friendly interface.
正解：A
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 # 42
A retail company with a large online catalog wants to improve customer experience and drive sales by implementing multimodal search capabilities (image, voice, and text). What is a primary business benefit of this capability?
- A. Reduced dependency on keyword optimization for product listings and improved search engine rankings.
- B. Streamlined inventory management processes and more accurate demand forecasting for popular items.
- C. Lowered operational costs associated with managing and updating product information across different platforms and channels.
- D. Improved customer engagement and product discovery leading to increased satisfaction and potential sales.
正解：D
解説：
Multimodal search directly enhances the customer experience by allowing them to find products using various intuitive methods (images, voice, text). This leads to easier product discovery, higher engagement, and ultimately increased customer satisfaction and potential sales, which is a primary business benefit.
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質問 # 43
A company is exploring Google Agentspace to improve how its employees search for information on their enterprise systems and automate certain tasks. What is the key business advantage of using Agentspace?
- A. Enhanced real-time communication and collaboration among team members.
- B. Greater interoperability with legacy software systems and databases.
- C. More granular control over support team access and permissions for sensitive data.
- D. Improved productivity and data interaction using AI assistants and advanced document analysis.
正解：D
解説：
Google Agentspace (or similar agent platforms) is designed to empower employees with AI-powered assistants that can navigate and interact with enterprise systems, analyze documents, and automate tasks. This directly leads to improved employee productivity and more efficient data interaction by leveraging AI to streamline workflows and provide faster access to information.
質問 # 44
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. Google Search
- B. Vertex AI Search
- C. Google Shopping
- D. Vertex AI Platform
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 # 45
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 uses human oversight to ensure accuracy before presenting information to the customer.
- 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 enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
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 # 46
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 control the creativity and randomness of the model's output by adjusting the diversity of word choices.
- C. To filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.
- D. To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.
正解：C
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 # 47
What is a key advantage of using Google's custom-designed TPUs?
- A. TPUs are specialized AI processors that excel at parallel processing for machine learning workloads.
- B. TPUs are lightweight processors intended for deployment on edge devices.
- C. TPUs are primarily designed to improve the general processing speed of virtual machines in the cloud.
- D. TPUs increase the storage capacity and data retrieval speeds within Google Cloud data centers.
正解：A
解説：
TPUs (Tensor Processing Units) are custom-designed hardware accelerators developed by Google specifically for high-performance machine learning tasks. Their advantage lies in their architecture, which is optimized for the massively parallel matrix multiplication operations that form the mathematical backbone of deep learning and large language models (LLMs).
TPUs excel at parallel processing (C) for training and running machine learning workloads, allowing computations to be performed simultaneously across numerous cores. This makes them significantly faster and more efficient than traditional CPUs or even general-purpose GPUs for tasks like training massive generative models (e.g., Gemini).
TPUs are a core component of the Infrastructure Layer in the Generative AI landscape, providing the foundational compute resources.
While Google offers very small, specialized TPUs for the edge (like Edge TPU), the primary, large-scale advantage is in the cloud for accelerating training and inference for complex ML models.
Options A describes the Edge TPU or Gemini Nano deployment strategy, not the general, key advantage. Options B and D misrepresent the function, as TPUs are compute hardware, not storage accelerators or general-purpose CPU replacements.
(Reference: Google's training materials on the Generative AI Infrastructure Layer explicitly list TPUs and GPUs as the physical hardware components providing the core computing resources needed for generative AI, with TPUs being specialized for accelerating ML workloads and parallel processing.)
質問 # 48
What is the definition of generative AI?
- A. A type of artificial intelligence that can create new content and ideas, including text, images, music, and code.
- B. A type of predictive model that estimates a relationship by fitting a line to the observed data.
- C. A type of artificial intelligence that enables a system to autonomously learn and improve using neural networks and deep learning.4
- D. A type of machine learning algorithm inspired by the human brain that is made up of interconnected nodes.
正解：A
解説：
The defining characteristic of generative AI is its ability to create new, original content that resembles its training data. This includes various modalities like text, images, music, and code, rather than just classifying, predicting, or analyzing existing data.
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質問 # 49
A company is developing a conversational AI chatbot. They need to ensure the chatbot can engage in human- like conversations and provide accurate information. What should they do to enhance thechatbot's ability to understand and respond effectively to user prompts?
- A. Lower model temperature setting to produce more consistent and predictable responses.
- B. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
- C. Limit the chatbot's training data to prevent it from learning irrelevant information.
- D. Use prompt engineering techniques, like few-shot prompting, to provide the chatbot with examples of successful interactions.
正解：D
解説：
Prompt engineering, especially techniques like few-shot prompting (providing examples of desired input- output pairs), is crucial for guiding a generative AI model to understand context and generate relevant, human- like responses. Limiting data or using strict keyword matching would severely restrict the chatbot's conversational ability, and lowering temperature makes responses less creative, not necessarily more understanding.
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質問 # 50
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. Prompt chaining that engages the AI in a conversation to gather the necessary information before generating the email response.
- B. Few-shot prompting that provides examples of good and bad customer service emails.
- C. Role prompting that instructs the AI to act as an experienced customer service representative with corporate knowledge.
- D. One-shot prompting that provides a single example of a good customer service email.
正解：C
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 # 51
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. Veo
- B. Gemini
- C. Imagen
- 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 # 52
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 expertiseand need a versatile solution.
Which Google foundation model should they use?
- A. Gemma
- B. Veo
- C. Gemini
- D. Imagen
正解：C
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 # 53
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?
- A. A conversational agent
- B. A customer service agent
- C. A workflow agent
- D. An employee productivity agent
正解：C
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 # 54
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. Vertex AI Natural Language API
- B. Vertex AI Conversation
- C. Pre-built RAG with Vertex AI Search
- D. Vertex AI Model Garden
正解：C
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 this indexed 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 # 55
A research company needs to analyze several lengthy PDF documents containing financial reports and identify key performance indicators (KPIs) and their trends over the past year. They want a Google Cloud prebuilt generative AI tool that can process these documents and provide summarized insights directly from the source material with citations. What should the analyst do?
- A. Create a custom Gem in Gemini Advanced with predefined KPIs to look across different financial reports.
- B. Use Gemini for Google Workspace within Google Docs to copy and paste sections of the reports for summary and analysis.
- C. Use the Gemini app to ask general financial trend questions.
- D. Use NotebookLM to upload and analyze the documents.
正解：D
解説：
The requirements are for a prebuilt tool that is designed for:
Analyzing uploaded private documents (lengthy PDFs).
Providing summarized insights (extracting KPIs and trends).
Offering citations (grounding the answers to the source material).
NotebookLM (C) is the Google tool explicitly designed for this use case. It is a generative AI powered notebook/research assistant that allows users to upload source documents (including PDFs), then ask questions and generate summaries or insights that are grounded in and cited back to the source documents. This makes it an ideal prebuilt solution for an analyst who needs to process complex, lengthy financial reports and verify the data with citations.
Gemini Advanced (A) and Gemini app (B) are general-purpose conversational tools that are not primarily focused on deep, grounded analysis of uploaded documents that require source citations for research integrity.
Gemini for Google Workspace (D) is limited to data already in Workspace apps (Docs, Gmail, Drive) and the manual copy/paste process would be inefficient for "several lengthy PDF documents." (Reference: Google's Generative AI Leader training materials highlight NotebookLM as the specific generative AI application built for research and information synthesis from uploaded documents, offering key features like grounding and citations back to the source material.)
質問 # 56
A company has a machine learning project that involves diverse data types like streaming data and structured databases. How does Google Cloud support data gathering for this project?
- A. Google Cloud provides tools such as Pub/Sub, Cloud Storage, and Cloud SQL.
- B. The Gemini app is the primary Google Cloud tool for directly collecting data.
- C. Google Cloud relies on Vertex AI to connect to external data.
- D. Google Cloud's strengths are in the data analysis tools such as BigQuery.
正解：A
解説：
Google Cloud offers a comprehensive suite of services for data ingestion and storage. Pub/Sub is for streaming data, Cloud Storage for various file types (including unstructured), and Cloud SQL for relational structured databases. These are fundamental for gathering diverse data. Gemini is a model, BigQuery is for analysis, and Vertex AI is for ML platform, not primary data collection tools themselves.
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質問 # 57
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. Seamless knowledge sharing and collaboration across internal systems.
- C. Improved IT infrastructure management across offices.
- D. Enhanced data encryption and compliance for internal communications.
正解：B
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 # 58
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. Secure-by-design infrastructure
- C. Identity and Access Management
- D. Workload monitoring tools
正解：C
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 # 59
A global news agency is developing a generative AI tool to quickly summarize breaking news articles as they emerge online. The goal is to provide their audience with rapid updates on fast-developing stories from various global sources. What Google Cloud solution should they use?
- A. Document AI
- B. Vertex AI Natural Language API
- C. Grounding with Google Search
- D. BigQuery
正解：C
解説：
For summarizing breaking news articles as they emerge online from various global sources, the generative AI model needs access to current, broad, and rapidly updating information. Grounding with Google Search allows the LLM to pull in the latest information from the web, ensuring the summaries are current and comprehensive. While Vertex AI Natural Language API can summarize text, it wouldn't inherently have access to the latest breaking news unless explicitly fed.
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質問 # 60
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?
- A. Data dependency
- B. Knowledge cutoff
- C. Hallucination
- D. Bias
正解：C
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. While a knowledge cutoff (B) is a common cause of hallucinations when an LLM is asked about recent events, the core limitation of fabricating facts from its own hardwired knowledge is the hallucination itself. Data dependency (A) relates to the model's reliance on the quality and completeness of its training data, and while flawed training data can be a cause, the error mode of inventing facts is the Hallucination.
質問 # 61
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