New APP Generative-AI-Leader Simulations | Generative-AI-Leader Exam Questions Vce
New APP Generative-AI-Leader Simulations | Generative-AI-Leader Exam Questions Vce
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Candidates who pass Generative-AI-Leader Certification prove their worth in the Google field. The Google Cloud Certified - Generative AI Leader Exam certification is proof of their competence and skill. This skill is highly useful in big Google companies that facilitate a candidate's career. To get certified, it is very important that you pass the Google Cloud Certified - Generative AI Leader Exam certification exam to prove your skills to the tech company. For this task, you require high-quality and accurate prep material to help you out. And many people don't get reliable material and ultimately fail. Failure leads to a loss of time and money.
The web-based Generative-AI-Leader practice exam is similar to the desktop-based software. You can take the web-based Generative-AI-Leader practice exam on any browser without needing to install separate software. In addition, all operating systems also support this web-based Google Generative-AI-Leader Practice Exam. Both Google Cloud Certified - Generative AI Leader Exam practice exams track your performance and help to overcome mistakes. Furthermore, you can customize your Google Cloud Certified - Generative AI Leader Exam practice exams according to your needs.
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Generative-AI-Leader Exam Questions Vce | Generative-AI-Leader Exam Discount Voucher
The latest Generative-AI-Leader dumps pdf covers every topic of the certification exam and contains the latest test questions and answers. By practicing our Generative-AI-Leader vce pdf, you can test your skills and knowledge for the test and make well preparation for the formal exam. One-year free updating will ensure you get the Latest Generative-AI-Leader Study Materials first time and the accuracy of our Generative-AI-Leader exam questions guarantee the high passing score.
Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q45-Q50):
NEW QUESTION # 45
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. Conversational Insights
- C. Google Cloud Contact Center as a Service
- 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 # 46
A company is defining their generative AI strategy. They want to follow Google-recommended practices to increase their chances of success. Which strategy should they use?
- A. Multi-directional strategy
- B. Rapid implementation strategy
- C. Bottom-up strategy
- D. Top-down strategy
Answer: D
Explanation:
Google Cloud often recommends a "top-down" approach for generative AI strategy. This means starting with clear business objectives and leadership alignment on how generative AI can solve critical business problems, rather than simply experimenting from the bottom up without a clear strategic direction.
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NEW QUESTION # 47
A company's large learning model (LLM) is producing hallucinations that are a result of the Knowledge cutoff. How does retrieval-augmented generation (RAG) overcome this limitation?
- A. RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
- B. RAG uses human oversight to ensure accuracy before presenting information to the customer.
- C. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
- D. RAG fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.
Answer: A
Explanation:
The primary purpose of RAG is to address the "knowledge cutoff" and hallucination issues of LLMs. It does this by retrieving relevant, up-to-date information from external knowledge sources (like databases or documents) at inference time and then using this retrieved information to ground the LLM's generation, ensuring factual accuracy and relevance to the specific query.
________________________________________
NEW QUESTION # 48
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 analyze situations, use multiple tools, and make informed decisions without requiring constant human input.
- B. To store and manage large datasets used for training and running gen AI models.
- C. To provide a user-friendly interface for interacting with gen AI models.
- D. To provide the computational resources needed to train and run gen AI models.
Answer: A
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.
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NEW QUESTION # 49
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. Vision AI
- B. Dataflow
- C. Document AI API
- D. Natural Language API
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.
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NEW QUESTION # 50
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