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| Section | Weight | Objectives |
|---|---|---|
| Google Cloud's Generative AI Offerings | 35% | - Enterprise integration and security features - Overview of Google Cloud generative AI services and tools - Vertex AI generative AI capabilities - Generative AI application development platforms - Model Garden and available models |
| Business Strategies for Successful Generative AI Solutions | 15% | - Identifying business use cases and value opportunities - Governance, risk management, and compliance - Planning and adoption frameworks - Scaling and measuring success of generative AI initiatives |
| Fundamentals of Generative AI | 30% | - Responsible AI principles and application - Foundation models: definition, capabilities, and use cases - Core concepts and characteristics of generative AI - Key technologies and differences from traditional AI |
| Techniques to Improve Generative AI Model Output | 20% | - Mitigation of bias and inaccuracies - Prompt engineering principles and best practices - Evaluation and optimization of output quality - Fine-tuning and adaptation methods |
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NEW QUESTION # 28
A large e-commerce company with a vast and frequently updated product catalog finds that customers struggle to find products on their website, and support agents spend too much time finding detailed product information. The company wants to improve search accuracy and efficiency for both customers and support. What Google Cloud solution should they use?
Answer: B
Explanation:
This scenario strongly points to the need for accurate and up-to-date information retrieval from a product catalog. Pre-built RAG (Retrieval-Augmented Generation) combined with Vertex AI Search is the ideal solution. Vertex AI Search can index the product catalog, and RAG can then use this indexed data to ground the responses of a generative AI model, ensuring that both customer searches and support agent queries retrieve precise and relevant product information.
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NEW QUESTION # 29
An organization wants to use generative AI to create a marketing campaign. They need to ensure that the AI model generates text that is appropriate for the target audience. What should the organization do?
Answer: B
Explanation:
Role prompting is a technique where you instruct the generative AI model to "act as" a specific persona or character. By assigning the model a role (e.g., "Act as a marketing expert writing for a young, tech-savvy audience"), you can guide its tone, style, and content to be appropriate for the target audience of the marketing campaign.
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NEW QUESTION # 30
What is the function of the platform layer in the generative AI (gen AI) landscape?
Answer: C
NEW QUESTION # 31
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?
Answer: A
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 # 32
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?
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.
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NEW QUESTION # 33
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