GOOGLE GENERATIVE-AI-LEADER NEW DUMPS & GENERATIVE-AI-LEADER EXAM TIPS

Google Generative-AI-Leader New Dumps & Generative-AI-Leader Exam Tips

Google Generative-AI-Leader New Dumps & Generative-AI-Leader Exam Tips

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Tags: Generative-AI-Leader New Dumps, Generative-AI-Leader Exam Tips, Generative-AI-Leader Exam Certification Cost, Generative-AI-Leader Authorized Exam Dumps, Exam Generative-AI-Leader Materials

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
A company is developing a conversational AI chatbot. They need to ensure the chatbot can engage in human- like conversations and provide accurate information. What should they do to enhance thechatbot's ability to understand and respond effectively to user prompts?

  • A. Limit the chatbot's training data to prevent it from learning irrelevant information.
  • B. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
  • C. Lower model temperature setting to produce more consistent and predictable responses.
  • D. Use prompt engineering techniques, like few-shot prompting, to provide the chatbot with examples of successful interactions.

Answer: D

Explanation:
Prompt engineering, especially techniques like few-shot prompting (providing examples of desired input- output pairs), is crucial for guiding a generative AI model to understand context and generate relevant, human- like responses. Limiting data or using strict keyword matching would severely restrict the chatbot's conversational ability, and lowering temperature makes responses less creative, not necessarily more understanding.
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NEW QUESTION # 23
A large e-commerce company with a substantial product catalog and many support documents has customers struggling to find information on their website. This leads to high support costs and poor user experience. The company wants a Google Cloud solution to improve website search and reduce support costs while improving customer satisfaction. What Google Cloud product should the company use?

  • A. Google Search
  • B. Vertex AI Platform
  • C. Google Shopping
  • D. Vertex AI Search

Answer: D

Explanation:
Vertex AI Search is ideal for this scenario. It allows companies to build sophisticated search experiences over their own product catalogs and support documents. This improves accuracy and helps customers find what they need, directly addressing high support costs and poor user experience. Vertex AI Platform is broader for general ML development, Google Shopping is for consumers, and Google Search is for the public web.
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NEW QUESTION # 24
A company wants to choose a generative AI (gen AI) use case that will be successful and have the most impact. What key factor should they determine first according to Google Cloud-recommended practices?

  • A. The frequency of updates to the underlying foundation models used by different gen AI platforms.
  • B. The specific business problems the company aims to solve and the desired outcomes.
  • C. The availability of pre-trained models that are offered on various cloud computing platforms.
  • D. The number of employees who will be trained to use the new gen AI tools.

Answer: B

Explanation:
A fundamental principle for successful AI adoption, including generative AI, is to start with clear business problems and desired outcomes. Without a well-defined problem, the AI solution might not deliver meaningful value, regardless of the technology used. This "problem-first" approach is crucial for impactful AI strategy.
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NEW QUESTION # 25
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. Unsupervised learning
  • B. Reinforcement learning
  • C. Deep learning
  • D. Supervised learning

Answer: B

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 # 26
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. Enhanced data encryption and compliance for internal communications.
  • B. Seamless knowledge sharing and collaboration across internal systems.
  • C. Automation of employee performance reviews using AI.
  • D. Improved IT infrastructure management across offices.

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.
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NEW QUESTION # 27
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