UPDATED AND ERROR-FREE 2PASS4SURE GOOGLE GENERATIVE-AI-LEADER EXAM QUESTIONS

Updated and Error-free 2Pass4sure Google Generative-AI-Leader Exam Questions

Updated and Error-free 2Pass4sure Google Generative-AI-Leader Exam Questions

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively. What machine learning should the company use?

  • A. Supervised learning
  • B. Deep learning
  • C. Unsupervised learning
  • D. Reinforcement learning

Answer: D

Explanation:
This scenario perfectly describes reinforcement learning. In reinforcement learning, an agent learns to make decisions by interacting with an environment, receiving1 rewards for desirable actions and penalties for undesirable ones,2 and iteratively improving its behavior through trial and error to maximize cumulative reward.
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NEW QUESTION # 24
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?

  • A. Data agent
  • B. Customer service agent
  • C. Security agent
  • D. Code agent

Answer: C

Explanation:
Given the tasks involve researching threats and creating detection rules, the most appropriate and specialized agent would be a Security agent. This type of agent would be pre-configured or easily adaptable to understand security-specific contexts, data, and actions within a CISO's domain.
________________________________________


NEW QUESTION # 25
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 pre-trained models and low- and no-code AI tools and services.
  • B. Google Cloud's collaborative AI community and support forums connect developers with AI experts.
  • C. Google Cloud's focus on continuous improvement provides access to the latest AI tools, features, and best practices.
  • D. Google Cloud's comprehensive training materials and tutorials to help developers.

Answer: A

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.
________________________________________


NEW QUESTION # 26
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 fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.
  • C. RAG uses human oversight to ensure accuracy before presenting information to the customer.
  • D. RAG enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.

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 # 27
What will Google Cloud's Agent Assist help a company achieve?

  • A. The ability to build and deploy deterministic and generative chatbot agents for automated customer support.
  • B. The ability to provide real-time assistance and recommended responses to live customer service agents during their interactions.
  • C. The infrastructure to provide an enterprise-grade contact center solution with omnichannel support, routing, and integration with CRM systems.
  • D. The ability to analyze conversational data to identify customer sentiment, common topics of discussion, and insights into agent performance and customer experience.

Answer: B

Explanation:
Google Cloud's Agent Assist is specifically designed to augment human customer service agents. It provides real-time suggestions, retrieves relevant information, and offers recommended responses to agents during live interactions, improving their efficiency and consistency.
________________________________________


NEW QUESTION # 28
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