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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
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 enhances the creative writing capabilities of the LLM to generate more engaging and informative responses.
B. RAG fine-tunes the LLM on specific customer query patterns to improve the speed and efficiency of response generation.
C. RAG enables the LLM to retrieve relevant and up-to-date information from knowledge sources.
D. RAG uses human oversight to ensure accuracy before presenting information to the customer.

Answer: C

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 # 73
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. Improved IT infrastructure management across offices.
C. Automation of employee performance reviews using AI.
D. Seamless knowledge sharing and collaboration across internal systems.

Answer: D

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.
________________________________________

NEW QUESTION # 74
What are core hardware components of the infrastructure layer in the generative AI landscape?

A. Tools and services for building AI models
B. TPUs and GPUs
C. Pre-trained models
D. User interfaces

Answer: B

Explanation:
The Generative AI landscape is often broken down into several functional layers: Applications, Agents, Platforms, Models, and Infrastructure.
The Infrastructure Layer is the foundation, providing the physical and virtual computing resources necessary to run and train the large models. These resources include servers, storage, networking, and most importantly, the specialized hardware accelerators required for high- volume, parallel computation.
The core hardware components are the Graphics Processing Units (GPUs) and the custom- designed Tensor Processing Units (TPUs) (A). These accelerators are optimized for the massive matrix operations fundamental to deep learning and Gen AI model training and inference.

NEW QUESTION # 75
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 few-shot prompting.
B. Use zero-shot prompting.
C. Use role prompting.
D. Use prompt chaining.

Answer: D

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.

NEW QUESTION # 76
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 dat

A. Use pre-built gen AI chatbots for inventory questions.
B. Use Google Cloud databases and Vertex AI for the agent to get live data.
C. Use Vertex AI Studio to fine-tune a model with sample inventory data.
D. Build a custom API instead of using the gen AI agent.
E. They want the most cost-effective solution. What should the organization do?

Answer: C

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 # 77
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