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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 Vertex AI Search to index the papers and enable keyword-based searches.
- B. Use Vertex AI Agent Builder to create a custom AI agent.
- C. Use Gemini for Google Workspace to facilitate collaborative document review.
- D. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
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
According to Google-recommended practices, when should generative AI be used to automate tasks?
- A. When tasks involve sensitive information or require human oversight
- B. When tasks are repetitive and rule-based.
- C. When tasks are complex and require strategic decision-making.
- D. When tasks are highly creative and require original thought.
正解：B
解説：
The strategic value of Generative AI (Gen AI) in a business context, as taught in Google's courses, is primarily to enhance efficiency and productivity by taking over tasks that consume significant employee time.
Gen AI excels in automating tasks that:
Are repetitive and time-consuming, such as drafting initial emails, summarizing long documents, or generating code snippets. Automating these routine tasks (C) frees employees to focus on higher-value activities (like building customer relationships or strategic planning).
Involve the generation of new content based on patterns learned from large datasets (e.g., text, images, code).
Options A and D represent high-value, strategic work-highly creative or complex strategic decision-making-where human judgment and oversight remain paramount. While Gen AI can assist with these (e.g., brainstorming creative ideas or providing data-backed insights), it is generally not recommended for full automation. Option B explicitly requires human oversight due to its sensitive nature. Therefore, the best fit for full or augmented automation for efficiency is the handling of routine, repeatable, and non-complex tasks.
(Reference: Google Cloud documentation on Gen AI adoption and efficiency states that Gen AI transforms work by automating repetitive and time-consuming tasks to free up time for strategic thinking and creativity.)
質問 # 30
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. Increase the token count for the model to allow for longer descriptions.
- B. Add details to the prompt about the audience, tone, and keywords.
- C. Train the model on a dataset of marketing materials from other eco-friendly brands.
- D. Lower the temperature setting of the model to produce more consistent results.
正解：B
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 # 31
An organization with a team of live customer service agents wants to improve agent efficiency and customer satisfaction during support interactions. They are looking for a tool that can provide real-time guidance to agents, suggest helpful information, and streamline the support process without fully automating customer conversations. Which component of Google's Customer Engagement Suite should they use?
- A. Google Cloud Contact Center as a Service
- B. Agent Assist
- C. Conversational Insights
- D. Conversational Agents
正解：B
解説：
As previously mentioned, Agent Assist is specifically designed for real-time support to human agents, providing them with suggestions and relevant information during live customer interactions. Conversational Agents (chatbots) automate interactions, Conversational Insights analyze conversations after they occur, and Contact Center as a Service is the broader infrastructure.
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質問 # 32
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. Implement stricter safety settings to filter out potentially misinterpreted content altogether.
- B. Incorporate a human-in-the-loop (HITL) review process to refine the summaries.
- C. Increase the temperature parameter of the model to encourage more varied and less literal interpretations.
- D. Decrease the output length of the summaries to make them more concise.
正解：B
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 # 33
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 prompt chaining.
- C. Use role prompting.
- D. Adjust the temperature parameter.
正解：C
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 # 34
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. Implement stricter safety settings to filter out potentially misinterpreted content altogether.
- B. Incorporate a human-in-the-loop (HITL) review process to refine the summaries.
- C. Increase the temperature parameter of the model to encourage more varied and less literal interpretations.
- D. Decrease the output length of the summaries to make them more concise.
正解：B
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 # 35
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. Ensuring that the AI application can automatically rank all candidates without requiring human review.
- C. Focusing on minimizing the processing time for each application to improve efficiency.
- 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 # 36
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 analyze customer interactions on social media and automatically generate sales pitches tailored to their public profiles.
- B. To draft emails and provide real-time insights about customer needs.
- 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：B
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 # 37
What is a key advantage of using Google's custom-designed TPUs?
- A. TPUs are primarily designed to improve the general processing speed of virtual machines in the cloud.
- B. TPUs are lightweight processors intended for deployment on edge devices.
- C. TPUs increase the storage capacity and data retrieval speeds within Google Cloud data centers.
- D. TPUs are specialized AI processors that excel at parallel processing for machine learning workloads.
正解：D
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 # 38
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 customized learning agent
- B. A learning management system (LMS)
- C. An AI-powered recommendation system for learning resources
- D. A large language model fine-tuned on educational content
正解：A
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 # 39
What is a characteristic of Google Cloud as a generative AI company?
- A. Google Cloud ensures that all generative AI models and data are completely secured and isolated from external networks.
- B. Google Cloud has an AI-first focus that enables innovation, with continuous updates and broad integration across its platform.
- C. Google Cloud provides fully autonomous AI agents that require zero configuration or management overhead.
- D. Google Cloud relies on proprietary, closed-source AI technologies for maximum security benefits.
正解：B
解説：
Google Cloud emphasizes an AI-first approach, integrating AI capabilities across its services and consistently innovating with new models and features. While security is a high priority, fully autonomous AI agents requiring zero configuration are generally not the norm, and "completely secured and isolated from external networks" is an oversimplification of cloud security models. Google also contributes to and supports open- source AI initiatives, not solely relying on proprietary closed-source technologies.
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質問 # 40
What will Google Cloud's Agent Assist help a company achieve?
- A. The ability to build and deploy deterministic and generative chatbot agents for automated customer support.
- B. The infrastructure to provide an enterprise-grade contact center solution with omnichannel support, routing, and integration with CRM systems.
- C. The ability to analyze conversational data to identify customer sentiment, common topics of discussion, and insights into agent performance and customer experience.
- D. The ability to provide real-time assistance and recommended responses to live customer service agents during their interactions.
正解：D
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 # 41
A company collects customer feedback through open-ended survey questions where customers can write detailed responses in their own words, such as "The product was easy to use, and the customer support was excellent, but the delivery took longer than expected." What type of data is this?
- A. Unstructured data
- B. Quantitative data
- C. Structured data
- D. Labeled data
正解：A
解説：
Data is typically classified into two main types: structured and unstructured.
Structured data is highly organized, formatted for a predefined data model, and easily searchable in tabular form (e.g., columns and rows in a database, like customer names, order IDs, or star ratings).
Unstructured data lacks a pre-defined format or organization.
The customer feedback described is a detailed, free-text response written in the customer's own words. This qualitative data, whether it is an email, an essay, or a long-form survey response, does not fit into fixed fields and requires advanced Natural Language Processing (NLP) or Generative AI techniques to extract meaning. Since the text is non-tabular and has no inherent structure enforced by the collection method, it is correctly classified as Unstructured Data.
Quantitative data (D) refers to numerical values that can be counted or measured. Labeled data (C) is data that has been tagged with a meaningful output category, which this raw feedback has not yet received.
(Reference: Google's Generative AI Study Guides define Unstructured Data as data that does not have a predefined structure or data model, such as text documents, images, audio, and video. Free-text responses in a survey are a primary example of unstructured data.)
質問 # 42
A company is using a language model to solve complex customer service inquiries. For a particular issue, the prompt includes the following instructions:
"To address this customer's problem, we should first identify the core issue they are experiencing. Then, we need to check if there are any known solutions or workarounds in our knowledge base. If a solution exists, we should clearly explain it to the customer. If not, we might need to escalate the issue to a specialist. Following these steps will help us provide a comprehensive and helpful response. Now, given the customer's message: 'My order hasn't arrived, and the tracking number shows no updates for a week,' what should be the next step in resolving this?" What type of prompting is this?
- A. Role-based
- B. Chain-of-thought
- C. Few-shot
- D. Zero-shot
正解：B
解説：
The prompt explicitly instructs the Large Language Model (LLM) to perform a step-by-step reasoning process before arriving at the final answer. The instructions lay out a sequential series of intermediate steps: "first identify," "then check," "if a solution exists, explain," "if not, escalate." This technique is known as Chain-of-Thought (CoT) Prompting. CoT is a powerful prompt engineering technique where the user or developer explicitly includes intermediate reasoning steps in the prompt. This guides the model to break down a complex, multi-step problem into smaller, manageable, logical steps, significantly improving its reasoning ability and the accuracy of its final output for complex queries like customer service troubleshooting or multi-step analysis.
Zero-shot (A) would be the raw question without any structure.
Few-shot (B) would involve providing examples of successfully solved problems.
Role-based (C) would involve assigning a persona (e.g., "Act as a customer service expert") but would not explicitly mandate the sequential process.
The inclusion of the explicit steps ("first identify," "then check," etc.) is the defining characteristic of Chain-of-Thought prompting.
(Reference: Google's courses on Prompt Engineering classify Chain-of-Thought prompting as the technique that improves reasoning by explicitly giving the model a series of sequential, intermediate steps to follow to arrive at a better answer for complex tasks.)
質問 # 43
A national bank is overwhelmed by customer inquiries across multiple channels and needs an AI-powered solution to provide seamless, consistent support, empower customer support agents, and improve service quality. What Google Cloud product should the bank use?
- A. Vertex AI Search
- B. Google Contact Center as a Service
- C. Gemini for Google Workspace
- D. Gemini for Google Cloud
正解：B
解説：
The bank's requirement is for a solution that provides seamless, consistent support across multiple channels and helps to empower customer support agents and improve service quality. This describes the need for a comprehensive, end-to-end customer service infrastructure.
Google Contact Center as a Service (CCaaS) is the full, cloud-native contact center solution offered by Google Cloud (part of the Customer Engagement Suite). It is specifically designed to unify customer interactions across various channels (phone, chat, web messaging) and provides the necessary infrastructure for routing, managing agent workflows, and ensuring a consistent and secure customer experience at scale. This solution goes beyond simply automating a chatbot.
While Vertex AI Search (A) can be used as a component within the solution to ground answers in an internal knowledge base, and Gemini for Google Workspace (B) can boost individual agent productivity, neither provides the comprehensive multi-channel contact center infrastructure that the scenario demands. The scale and nature of the problem-unifying overwhelmed support across channels and empowering agents-requires an enterprise-grade platform, which is precisely the function of Google Contact Center as a Service.
質問 # 44
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 enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
- C. RAG fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.
- D. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
正解：B
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 # 45
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. Imagen
- B. Gemini
- C. Veo
- D. Gemma
正解：B
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 # 46
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. Establishing ethical guidelines for AI model responses to ensure fairness and avoid harm.
- B. Implementing access controls and protecting sensitive information within the training data.
- C. Monitoring the AI model's performance for unexpected outputs and potential errors.
- D. Applying the latest software patches to the AI model on a regular basis.
正解：B
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 # 47
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. Limit the chatbot's training data to prevent it from learning irrelevant information.
- B. Lower model temperature setting to produce more consistent and predictable responses.
- C. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
- 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 # 48
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. Security Command Center
- B. Identity and Access Management
- C. Secure-by-design infrastructure
- D. Workload monitoring tools
正解：A
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 # 49
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. Document AI API
- B. Vision AI
- C. Dataflow
- D. Natural Language API
正解：A
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 # 50
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. Fine-tune the gen AI model.
- B. Collect a larger and more diverse dataset for the gen AI model.
- C. Implement explainable gen AI policies.
- D. Develop fairness assessments for the gen AI model.
正解：C
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 # 51
A company trains a generative AI model designed to classify customer feedback as positive, negative, or neutral. However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don't reflect current customer communication styles. When the model is deployed, it shows a strong bias in its sentiment analysis for new customer feedback, misclassifying reviews from underrepresented demographics and struggling to understand current slang or phrasing. What type of model limitation is this?
- A. Hallucination
- B. Overfitting
- C. Edge case
- D. Data dependency
正解：D
解説：
The core reason for the model's failure is that the training data itself was flawed (disproportionate demographic representation and outdated language). This flaw directly leads to the observed bias and poor performance on underrepresented groups and modern communication styles.
This is a classic example of Data Dependency, a fundamental limitation of all machine learning models, including generative AI. Data dependency refers to the absolute reliance of an AI model on the quality, completeness, and fairness of the data on which it was trained. Since the model essentially only mimics the patterns it learned from its dataset, if the dataset contains societal, demographic, or linguistic biases, the model will faithfully reproduce and amplify those biases in its output, leading to unfair classification for certain groups.
Hallucination (C) is the invention of facts or data.
Overfitting (D) is poor generalization because the model memorized the training data too well, typically resulting in very poor performance across all unseen data, not just specific demographics.
Bias is the result of the data dependency, not the fundamental limitation itself.
(Reference: Google's training on Generative AI Limitations identifies Data Dependency as the fundamental limitation where the model is limited by the scope and quality of its training data, directly leading to issues of bias when the data is not diverse or representative.)
質問 # 52
An organization wants to use generative AI to create a chatbot that can answer customer questions about their account balances. They need to ensure that the chatbot can access previous portions of the conversation with the customer. Which prompting technique should they use?
- A. Use few-shot prompting.
- B. Use prompt chaining.
- C. Use zero-shot prompting.
- D. Use role prompting.
正解：B
解説：
Prompt chaining (or conversational memory/context management) is the technique used to maintain the conversational context. It involves feeding previous turns of a conversation (or a summary of them) back into the model along with the current user query, allowing the chatbot to "remember" and reference past interactions for coherent and contextually relevant responses, especially crucial for tasks like checking account balances that span multiple turns.
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質問 # 53
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