100% PASS RATE GENERATIVE-AI-LEADER RELIABLE EXAM GUIDE COVERS THE ENTIRE SYLLABUS OF GENERATIVE-AI-LEADER

100% Pass Rate Generative-AI-Leader Reliable Exam Guide Covers the Entire Syllabus of Generative-AI-Leader

100% Pass Rate Generative-AI-Leader Reliable Exam Guide Covers the Entire Syllabus of Generative-AI-Leader

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
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 Conversation
  • B. Vertex AI Model Garden
  • C. Vertex AI Natural Language API
  • D. Pre-built RAG with Vertex AI Search

Answer: D

Explanation:
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 thisindexed 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.
________________________________________


NEW QUESTION # 40
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. Cloud Storage
  • B. BigQuery
  • C. Vertex AI Pipelines
  • D. Model Registry

Answer: D

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.
________________________________________


NEW QUESTION # 41
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. copyright
  • B. Imagen
  • C. Veo
  • D. Gemma

Answer: D

Explanation:
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, copyright is typically larger and more general-purpose, and Imagen is for image generation.
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NEW QUESTION # 42
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. The copyright app is the primary Google Cloud tool for directly collecting data.
  • B. Google Cloud relies on Vertex AI to connect to external data.
  • C. Google Cloud provides tools such as Pub/Sub, Cloud Storage, and Cloud SQL.
  • D. Google Cloud's strengths are in the data analysis tools such as BigQuery.

Answer: C

Explanation:
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. copyright is a model, BigQuery is for analysis, and Vertex AI is for ML platform, not primary data collection tools themselves.
________________________________________


NEW QUESTION # 43
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. Natural Language API
  • C. Vision AI
  • D. Dataflow

Answer: A

Explanation:
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.
________________________________________


NEW QUESTION # 44
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