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Google Generative-AI-Leader Exam Syllabus Topics:

Topic
Details

Topic 1

• Implementing AV Solutions: This section of the exam measures the skills of an AV Integration Technician and focuses on turning AV system designs into reality. It assesses the ability to integrate systems by verifying components and managing supply installations, as well as handling documentation, training, and creating as-built schematics that support ongoing system operation.

Topic 2

• Creating AV Solutions: This section of the exam measures the skills of an AV Systems Designer and covers the processes involved in understanding client requirements and translating them into practical AV solutions. It includes tasks such as conducting customer needs analysis, performing site surveys to evaluate conditions like lighting and acoustics, developing AV project scopes, and designing system layouts and documentation.

Topic 3

• Supporting AV System Operation: This section of the exam measures the skills of an AV Support Specialist and focuses on providing assistance in operating audiovisual systems. It includes remote and onsite troubleshooting, training users, and delivering live event support, ensuring systems function effectively in real-world usage scenarios.

Topic 4

• Servicing AV Solutions: This section of the exam measures skills of an AV Maintenance Technician and focuses on maintaining and repairing AV systems. Tasks include supervising operations, conducting routine maintenance such as firmware updates and component replacements, and resolving issues through troubleshooting and repair processes, ensuring long-term system performance.

 

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
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. Conversational Agents
• B. Google Cloud Contact Center as a Service
• C. Conversational Insights
• D. Agent Assist

Answer: D

Explanation:
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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NEW QUESTION # 41
A pharmaceutical company's research and development department spends significant time manually reviewing new scientific papers to identify potential drug targets. They need a solution that can answer questions about these documents and provide summarized insights to researchers without requiring extensive coding expertise. What should the organization do?

• A. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.
• B. Use Vertex AI Search to index the papers and enable keyword-based searches.
• C. Use Vertex AI Agent Builder to create a custom AI agent.
• D. Use Gemini for Google Workspace to facilitate collaborative document review.

Answer: C

Explanation:
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.
________________________________________

 

NEW QUESTION # 42
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

Answer: B

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.
________________________________________

 

NEW QUESTION # 43
A company wants to use an AI agent to automate some tasks. They want everyone to understand the different functions of an AI agent. What is the function of an AI agent in the context of gen AI?

• A. To store and manage large datasets used for training and running gen AI models.
• B. To provide the computational resources needed to train and run gen AI models.
• C. To analyze situations, use multiple tools, and make informed decisions without requiring constant human input.
• D. To provide a user-friendly interface for interacting with gen AI models.

Answer: C

Explanation:
An AI agent, especially in the context of generative AI, is designed to be more autonomous and capable than a simple model. Its function is to understand a goal, analyze a situation, leverage various tools (including other generative AI models or external APIs), and make decisions or take actions to achieve that goal, often with minimal human intervention.
________________________________________

 

NEW QUESTION # 44
A highly regulated financial institution wants to use Gemini as the core decision engine for a loan approval system that will deterministically approve or reject loan applications based on a strict set of predefined criteria. Why is this an inappropriate use case for Gemini?

• A. Gemini is designed for flexible content generation and inference, not rigid rule-based decisions.
• B. Gemini is not equipped to handle structured numerical data for financial assessments.
• C. Gemini deployment for this scenario would be too expensive and complex.
• D. Gemini cannot integrate with required financial databases.

Answer: A

Explanation:
Gemini, as a large language model, excels at flexible content generation, summarization, understanding, and inference. However, it is not designed for deterministic, rule-based decision-making that requires absolute consistency and adherence to strict, predefined criteria, as is common in highly regulated financial systems like loan approvals. Such systems typically require traditional programming logic or specific rule engines for auditable and consistent outcomes.
________________________________________

 

NEW QUESTION # 45
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