PASS GUARANTEED QUIZ 2025 GOOGLE GENERATIVE-AI-LEADER: GOOGLE CLOUD CERTIFIED - GENERATIVE AI LEADER EXAM LATEST RELIABLE EXAM COST

Pass Guaranteed Quiz 2025 Google Generative-AI-Leader: Google Cloud Certified - Generative AI Leader Exam Latest Reliable Exam Cost

Pass Guaranteed Quiz 2025 Google Generative-AI-Leader: Google Cloud Certified - Generative AI Leader Exam Latest Reliable Exam Cost

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
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 prompt engineering techniques, like few-shot prompting, to provide the chatbot with examples of successful interactions.
  • C. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
  • D. Lower model temperature setting to produce more consistent and predictable responses.

Answer: B

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 # 13
A company wants to create an AI-powered educational solution that provides personalized learning experiences for students. This platform will assess a student's knowledge, recommend relevant learning materials, and generate personalized exercises. The application would provide the structure for lessons and track progress. What type of AI solution should they use?

  • A. A customized learning agent
  • B. An AI-powered recommendation system for learning resources
  • C. A learning management system (LMS)
  • D. A large language model fine-tuned on educational content

Answer: A

Explanation:
The request goes beyond just recommendations or content generation. It involves assessing knowledge, recommending materials, generating personalized exercises, providing lesson structure, and tracking progress.
This implies a more comprehensive, intelligent system that acts as an assistant or tutor for the student, which is best described as a customized learning agent. This agent would likely leverage LLMs and recommendation systems as components, but the overall solution is an agent.
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NEW QUESTION # 14
A financial institution uses generative AI (gen AI) to approve and reject loan applications, but gives no reasons for rejection. Customers are starting to file complaints. The company needs to implement a solution to reduce the complaints. What should the company do?

  • A. Fine-tune the gen AI model.
  • B. Collect a larger and more diverse dataset for the gen AI model.
  • C. Implement explainable gen AI policies.
  • D. Develop fairness assessments for the gen AI model.

Answer: C

Explanation:
The core problem is the lack of reasons for rejection, leading to customer complaints. This falls under the domain of explainable AI (XAI). Implementing explainable gen AI policies or mechanisms would allow the institution to provide transparency into how the AI made its decision, addressing the customer complaints directly. While other options might improve the model, they don't directly solve the transparency issue.
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NEW QUESTION # 15
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. Enhanced data encryption and compliance for internal communications.
  • C. Seamless knowledge sharing and collaboration across internal systems.
  • D. Improved IT infrastructure management across offices.

Answer: C

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 # 16
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 focus on continuous improvement provides access to the latest AI tools, features, and best practices.
  • C. Google Cloud's collaborative AI community and support forums connect developers with AI experts.
  • 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.
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NEW QUESTION # 17
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