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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of Generative AI | 30% | - Key technologies and differences from traditional AI - Core concepts and characteristics of generative AI - Responsible AI principles and application - Foundation models: definition, capabilities, and use cases |
| Topic 2: Google Cloud's Generative AI Offerings | 35% | - Model Garden and available models - Generative AI application development platforms - Overview of Google Cloud generative AI services and tools - Vertex AI generative AI capabilities - Enterprise integration and security features |
| Topic 3: Business Strategies for Successful Generative AI Solutions | 15% | - Identifying business use cases and value opportunities - Scaling and measuring success of generative AI initiatives - Planning and adoption frameworks - Governance, risk management, and compliance |
| Topic 4: Techniques to Improve Generative AI Model Output | 20% | - Evaluation and optimization of output quality - Fine-tuning and adaptation methods - Prompt engineering principles and best practices - Mitigation of bias and inaccuracies |
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NEW QUESTION # 98
A company wants to use generative AI to create a chatbot that can answer customer questions about their products and services. They need to ensure that the chatbot only uses information from the company's official documentation. What should the company do?
Answer: C
Explanation:
The core requirement is to guarantee that the chatbot only uses information from the company's official documentation and does not rely on its general knowledge base. This is crucial for ensuring factual accuracy, relevance to the company's specific products, and preventing the generation of fabricated or incorrect information (hallucinations).
The specific technique designed to address this challenge is Grounding. Grounding is the process of connecting the Large Language Model's (LLM's) responses to a trusted, verifiable source of information, such as an organization's internal documents, databases, or live data feeds. When an LLM is grounded, it is forced to base its answers only on the provided context, effectively preventing it from drawing on its broad, generalized training data. Grounding is often implemented using a method called Retrieval-Augmented Generation (RAG), particularly with tools like Google Cloud's Vertex AI Search, which indexes the official documentation and feeds the relevant snippets to the model.
NEW QUESTION # 99
A company is defining their generative AI strategy. They want to follow Google-recommended practices to increase their chances of success. Which strategy should they use?
Answer: A
Explanation:
Google Cloud's adoption frameworks advise an executive-sponsored, top-down strategy when initiating generative AI programs. A top-down approach ensures executive alignment with tangible business objectives, formalizes risk and governance controls, allocates appropriate cloud infrastructure and data resources, and prioritizes use cases delivering high business value rather than disconnected experimentation.
NEW QUESTION # 100
A company's customer service chatbot is built on limited rules and struggles with complex customer queries, which causes customer frustration. They want to try different generative AI models and modify them to improve performance, but their current AI setup makes this difficult and costly. Which Google Cloud benefit would help solve this problem?
Answer: C
Explanation:
Google Cloud provides access to multiple pre-trained foundation models that organizations can evaluate and adapt without developing a model from the beginning. Tunable models allow the company to modify model behavior using domain-specific examples so the chatbot can better address complex customer questions, follow the preferred response style, and use relevant terminology. This directly resolves the difficulty and expense of experimenting with and customizing models. Integrated MLOps tools help manage development, deployment, monitoring, and lifecycle processes, but they do not primarily supply the model choice and customization requested. Comprehensive security protects systems and data, while AI-optimized infrastructure supplies scalable computation. Both are valuable but do not directly address this scenario. Therefore, access to pre-trained and tunable models is the Google Cloud benefit most closely aligned with the company's requirements.
NEW QUESTION # 101
A marketing team wants to use a foundation model to create social media and advertising campaigns. They want to create written articles and images from text. They lack deep AI expertise and need a versatile solution. Which Google foundation model should they use?
Answer: B
Explanation:
Gemini is Google's most advanced and multimodal foundation model, capable of understanding and generating various forms of content, including text and images, from a single prompt. Its versatility makes it suitable for marketing teams that need to create diverse campaign materials without deep AI expertise. Imagen is specifically for image generation, Gemma is a family of smaller, open models, and Veo is for video generation.
NEW QUESTION # 102
A company has launched a new generative AI-powered chatbot on its customer service portal to handle frequently asked questions. They want to monitor the new chatbot's performance over time to ensure it continues to perform well. What Google-recommended practice should they use?
Answer: D
Explanation:
To evaluate and track the sustained performance and business impact of an AI system over time, organizations must establish, measure, and monitor quantitative Key Performance Indicators (KPIs). For customer support agents, standard KPIs include resolution rate, containment rate, deflection rate, latency, user satisfaction (CSAT), and error/escalation rates.
NEW QUESTION # 103
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