RELIABLE GENERATIVE-AI-LEADER BRAINDUMPS - SURE GENERATIVE-AI-LEADER PASS

Reliable Generative-AI-Leader Braindumps - Sure Generative-AI-Leader Pass

Reliable Generative-AI-Leader Braindumps - Sure Generative-AI-Leader Pass

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
A logistics company wants to use a generative AI (gen AI) agent to automatically check real-time inventory levels across its warehouses and adjust delivery schedules. The gen AI agent needs access to internal inventory data. They want the most cost-effective solution. What should the organization do?

  • A. Use Google Cloud databases and Vertex AI for the agent to get live data.
  • B. Use Vertex AI Studio to fine-tune a model with sample inventory data.
  • C. Build a custom API instead of using the gen AI agent.
  • D. Use pre-built gen AI chatbots for inventory questions.

Answer: A

Explanation:
To achieve real-time inventory checks and adjust delivery schedules, the generative AI agent needs live access to the company's internal inventory data. Google Cloud databases provide the structured storage for this data, and Vertex AI offers the platform to build, deploy, and manage the AI agent, including connecting it to these live data sources. This approach allows the agent to make informed decisions based on current information. Building a custom API for every interaction might be less cost-effective in the long run for dynamic inventory data. Pre-built chatbots might not have the direct integration needed for real-time adjustments, and fine-tuning with sample data wouldn't provide the live data access required.


NEW QUESTION # 28
A software development team wants to use generative AI (gen AI) to code faster so they can launch their software prototype quicker. What should the team do?

  • A. Use gen AI to automatically generate comprehensive documentation for their code.
  • B. Use gen AI to suggest code snippets and complete functions.
  • C. Use gen AI to identify potential bugs and security vulnerabilities in their code.
  • D. Use gen AI to refactor and optimize existing code.

Answer: B

Explanation:
While generative AI can assist with all the options listed (refactoring, documentation, bug identification), its most direct and significant impact on coding faster for a prototype is through code generation. Suggesting code snippets and completing functions directly accelerates the writing of new code, enabling quicker prototyping.
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NEW QUESTION # 29
An organization wants to use generative AI to create a chatbot that can answer customer questions about their account balances. They need to ensure that the chatbot can access previous portions of the conversation with the customer. Which prompting technique should they use?

  • A. Use prompt chaining.
  • B. Use zero-shot prompting.
  • C. Use role prompting.
  • D. Use few-shot prompting.

Answer: A

Explanation:
Prompt chaining (or conversational memory/context management) is the technique used to maintain the conversational context. It involves feeding previous turns of a conversation (or a summary of them) back into the model along with the current user query, allowing the chatbot to "remember" and reference past interactions for coherent and contextually relevant responses, especially crucial for tasks like checking account balances that span multiple turns.
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NEW QUESTION # 30
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.
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NEW QUESTION # 31
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 collaborative AI community and support forums connect developers with AI experts.
  • B. Google Cloud's comprehensive training materials and tutorials to help developers.
  • C. Google Cloud's focus on continuous improvement provides access to the latest AI tools, features, and best practices.
  • D. Google Cloud's pre-trained models and low- and no-code AI tools and services.

Answer: D

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 # 32
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