Exam Generative-AI-Leader Lab Questions | Generative-AI-Leader Latest Dumps Ebook
Exam Generative-AI-Leader Lab Questions | Generative-AI-Leader Latest Dumps Ebook
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Tags: Exam Generative-AI-Leader Lab Questions, Generative-AI-Leader Latest Dumps Ebook, Dumps Generative-AI-Leader Vce, Related Generative-AI-Leader Exams, Updated Generative-AI-Leader Demo
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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q29-Q34):
NEW QUESTION # 29
A company wants a generative AI platform that provides the infrastructure, tools, and pre-trained models needed to build, deploy, and manage its generative AI solutions. Which Google Cloud offering should the company use?
- A. BigQuery
- B. Vertex AI
- C. Google Cloud Storage
- D. Google Kubernetes Engine (GKE)
Answer: B
Explanation:
Vertex AI is Google Cloud's unified machine learning platform that provides end-to-end support for the ML lifecycle, including access to pre-trained models (foundation models), tools for fine-tuning, deployment, and management of generative AI solutions. BigQuery is a data warehouse, GKE is for container orchestration, and Cloud Storage is for object storage; while they might be components used with Vertex AI, they are not the comprehensive generative AI platform themselves.
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NEW QUESTION # 30
A large multinational corporation with geographically dispersed teams struggles with knowledge silos and inconsistent access to crucial internal information. What is a key business benefit of using Google Agentspace in this scenario?
- A. Automation of employee performance reviews using AI.
- B. Seamless knowledge sharing and collaboration across internal systems.
- C. Improved IT infrastructure management across offices.
- D. Enhanced data encryption and compliance for internal communications.
Answer: B
Explanation:
Google Agentspace (or similar agent-based frameworks) aims to connect and orchestrate various AI capabilities and data sources. In a scenario with knowledge silos, a key benefit would be to enable seamless knowledge sharing and collaboration by allowing agents to access, process, and disseminate information across different internal systems and teams.
________________________________________
NEW QUESTION # 31
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. Model Registry
- B. BigQuery
- C. Vertex AI Pipelines
- D. Cloud Storage
Answer: A
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 # 32
An organization needs an AI tool to analyze and summarize lengthy customer feedback text transcripts. You need to choose a Google foundation model with a large context window. What foundation model should the organization choose?
- A. CodeGemma
- B. Chirp
- C. Imagen
- D. copyright
Answer: D
Explanation:
copyright models are known for their large context windows, making them highly suitable for processing and summarizing lengthy texts like customer feedback transcripts. CodeGemma is specialized for code, Imagen for image generation, and Chirp for speech.
________________________________________
NEW QUESTION # 33
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. Natural Language API
- B. Dataflow
- C. Document AI API
- D. Vision AI
Answer: C
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 # 34
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