Google GENERATIVE-AI-LEADER Online Practice Questions and Exam Preparation

Google GENERATIVE-AI-LEADER Online Questions & Answers

• Question 1:

  A company's sales team spends a significant amount of time researching potential leads and manually entering data into their customer relationship management (CRM) tool. They want to improve the team's efficiency and enable them to focus on building relationships and closing deals. What should the organization do?

  A. Develop a custom AI solution using Google Cloud's AutoML Natural Language to analyze lead communications and automatically update the CRM.
  B. Implement Google Cloud's Contact Center AI to qualify leads and route them to the appropriate sales representatives.
  C. Implement Google Agentspace "unified enterprise search" including a CRM agent to automate lead research and data entry.
  D. Integrate the CRM with a popular sales intelligence platform to automatically enrich lead profiles with valuable data.
  C. Implement Google Agentspace "unified enterprise search" including a CRM agent to automate lead research and data entry.

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  explanation:

  Agentspace's unified search and specialized CRM agent can scour internal and external data sources, enrich lead profiles, and push updates directly into the CRM, freeing your sales team to focus on selling.

• Question 2:

  What is the definition of an AI agent?

  A. An application that learns how to achieve a goal based on inputs and tools available to it.
  B. A human expert who oversees and manages the operation of a gen AI application.
  C. A type of user interface that allows users to interact with a gen AI application.
  D. A static component of a gen AI application that provides information to the user.
  A. An application that learns how to achieve a goal based on inputs and tools available to it.

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  explanation:

  An AI agent autonomously uses its inputs, environment, and integrated tools to learn strategies and take actions toward achieving specified objectives.

• Question 3:

  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. Gemini for Google Workspace
  C. Google Contact Center as a Service
  D. Gemini for Google Cloud
  C. Google Contact Center as a Service

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  explanation:

  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.

• Question 4:

  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. Role prompting that instructs the AI to act as an experienced customer service representative with corporate knowledge.
  C. One-shot prompting that provides a single example of a good customer service email.
  D. Few-shot prompting that provides examples of good and bad customer service emails.
  B. Role prompting that instructs the AI to act as an experienced customer service representative with corporate knowledge.

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  explanation:

  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.)

• Question 5:

  A company is developing a generative AI-powered customer support chatbot. They want to ensure the chatbot can answer a wide range of customer questions accurately, even those related to recently updated product information not present in the model's original training data. What is a key benefit of implementing retrieval- augmented generation (RAG) in this chatbot?

  A. RAG will significantly reduce the computational resources required to run the generative AI model.
  B. RAG will primarily help the chatbot generate more creative and engaging conversational responses.
  C. RAG will enable the chatbot to fine-tune its underlying language model on the fly based on customer interactions.
  D. RAG will enable the chatbot to access and utilize external, up-to-date knowledge sources to provide more accurate and relevant answers.
  D. RAG will enable the chatbot to access and utilize external, up-to-date knowledge sources to provide more accurate and relevant answers.

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  explanation:

  The central problem is the Large Language Model's (LLM's) knowledge cutoff, where it cannot answer questions about information that appeared after its training data was collected (e.g., recently updated product details).

  Retrieval-Augmented Generation (RAG) is specifically designed to overcome this limitation. The process involves:

  Retrieval: When a question is asked, the RAG system first searches an external, up-to-date knowledge source (like a vector database of current product docs).

  Augmentation: It retrieves the most relevant, recent text snippets (the context).

  Generation: This retrieved context is added to the user's prompt (augmentation) and sent to the LLM, forcing the model to ground its response in the current facts.

  The key benefit is thus to enable the chatbot to access and utilize external, up-to-date knowledge sources (D). This ensures the answers are accurate and relevant to the most current product information, directly addressing the knowledge cutoff issue without requiring expensive model retraining.

  Option B is the function of the Temperature setting, not RAG.

  Option C describes an unproven and unscalable model update mechanism (fine-tuning is a separate process).

  RAG is a process enhancement that prioritizes accuracy and relevance over merely reducing computation (A).

  (Reference: Google Cloud documentation on RAG states that its primary purpose is to address the "knowledge cutoff" and hallucination issues of LLMs by retrieving relevant and up-to-date information from external knowledge sources at inference time and using this retrieved information to ground the LLM's generation, ensuring factual accuracy.)

• Question 6:

  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. Vertex AI Platform
  C. Google Shopping
  D. Google Search
  A. Vertex AI Search

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  explanation:

  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.

• Question 7:

  A global news company is using a large language model to automatically generate summaries of news articles for their website. The model's summary of an international summit was accurate until it hallucinated by stating a detail that did not occur. How should the company overcome this hallucination?

  A. Implement stricter safety settings to filter out potentially controversial topics.
  B. Fine-tune the model on a larger dataset of news articles.
  C. Increase the temperature setting of the model to encourage more diverse outputs.
  D. Use grounding to base the model output on the source articles.
  D. Use grounding to base the model output on the source articles.

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  explanation:

  The core problem is the model's hallucination--it invented a factual detail--in a context (news reporting) where factual accuracy is non-negotiable. To correct a factual error in a generative summary, the model must be constrained to speak only based on verifiable facts from a reliable source.

  The most effective technique to combat hallucinations and ensure factual adherence is Grounding (D). Grounding connects the Large Language Model's (LLM's) output to a specific, trusted, and verifiable source of information. This is often implemented using Retrieval-Augmented Generation (RAG). In this scenario, grounding the summary model on the original source articles ensures that every generated statement is directly entailed by the provided facts (the source article content).

  Option B, fine-tuning, is expensive and only updates the model's general knowledge and style; it does not prevent the model from guessing or fabricating details when retrieving information. Option C, increasing temperature, would make the output less consistent and more diverse, likely increasing the chance of hallucination, which is the opposite of the desired effect. Option A is unrelated to factual accuracy. Therefore, Grounding is the necessary step to anchor the model's responses to the true content of

  the source articles.

  (Reference: Google Cloud documentation on RAG/Grounding emphasizes that its primary purpose is to address the "knowledge cutoff" and hallucination issues of LLMs by retrieving relevant, up-to-date information from external knowledge sources and using this retrieved information to ground the LLM's generation, ensuring factual accuracy.)

• Question 8:

  What are core hardware components of the infrastructure layer in the generative AI landscape?

  A. TPUs and GPUs
  B. User interfaces
  C. Pre-trained models
  D. Tools and services for building AI models
  A. TPUs and GPUs

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  explanation:

  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.)

• Question 9:

  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. Build a custom API instead of using the gen AI agent.
  B. Use pre-built gen AI chatbots for inventory questions.
  C. Use Vertex AI Studio to fine-tune a model with sample inventory data.
  D. Use Google Cloud databases and Vertex AI for the agent to get live data.
  D. Use Google Cloud databases and Vertex AI for the agent to get live data.

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  explanation:

  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.

• Question 10:

  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. Improved productivity and data interaction using AI assistants and advanced document analysis.
  D. More granular control over support team access and permissions for sensitive data.
  C. Improved productivity and data interaction using AI assistants and advanced document analysis.

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  explanation:

  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.