GENERATIVE-AI-LEADER TEST COLLECTION PDF - EXAM GENERATIVE-AI-LEADER TOPIC

Generative-AI-Leader Test Collection Pdf - Exam Generative-AI-Leader Topic

Generative-AI-Leader Test Collection Pdf - Exam Generative-AI-Leader Topic

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
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. Veo
  • B. Gemma
  • C. Imagen
  • D. copyright

Answer: D

Explanation:
copyright 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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NEW QUESTION # 44
A company's development team is eager to start building generative AI solutions with Google Cloud, but has limited experience in AI development. They need to launch their gen AI solution quickly. What Google Cloud benefit would help the company achieve their goal?

  • A. Google Cloud's focus on continuous improvement provides access to the latest AI tools, features, and best practices.
  • B. Google Cloud's pre-trained models and low- and no-code AI tools and services.
  • C. Google Cloud's collaborative AI community and support forums connect developers with AI experts.
  • D. Google Cloud's comprehensive training materials and tutorials to help developers.

Answer: B

Explanation:
For a team with limited AI experience needing to launch quickly, leveraging pre-trained models (foundation models) and low-code/no-code tools significantly reduces the development burden and accelerates time to market. This allows them to build and deploy generative AI solutions without requiring deep expertise from scratch. While other options are helpful, this directly addresses the need for quick launch with limited experience.
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NEW QUESTION # 45
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 copyright for Google Workspace to facilitate collaborative document review.
  • C. Use Vertex AI Search to index the papers and enable keyword-based searches.
  • D. Use Vertex AI AutoML to train a model that classifies papers into predefined research areas.

Answer: A

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. copyright 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 # 46
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. Vertex AI Pipelines
  • B. BigQuery
  • C. Model Registry
  • D. Cloud Storage

Answer: C

Explanation:
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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NEW QUESTION # 47
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. More granular control over support team access and permissions for sensitive data.
  • B. Improved productivity and data interaction using AI assistants and advanced document analysis.
  • C. Enhanced real-time communication and collaboration among team members.
  • D. Greater interoperability with legacy software systems and databases.

Answer: B

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.


NEW QUESTION # 48
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