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| Section | Weight | Objectives |
|---|---|---|
| Business Strategies for Successful Generative AI Solutions | 15% | - Planning and adoption frameworks - Identifying business use cases and value opportunities - Scaling and measuring success of generative AI initiatives - Governance, risk management, and compliance |
| Techniques to Improve Generative AI Model Output | 20% | - Evaluation and optimization of output quality - Prompt engineering principles and best practices - Mitigation of bias and inaccuracies - Fine-tuning and adaptation methods |
| Fundamentals of Generative AI | 30% | - Foundation models: definition, capabilities, and use cases - Core concepts and characteristics of generative AI - Key technologies and differences from traditional AI - Responsible AI principles and application |
| Google Cloud's Generative AI Offerings | 35% | - Enterprise integration and security features - Generative AI application development platforms - Model Garden and available models - Overview of Google Cloud generative AI services and tools - Vertex AI generative AI capabilities |
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46. Frage
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?
Antwort: B
Begründung:
Google Cloud often recommends a "top-down" approach for generative AI strategy. This means starting with clear business objectives and leadership alignment on how generative AI can solve critical business problems, rather than simply experimenting from the bottom up without a clear strategic direction.
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47. Frage
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 expertiseand need a versatile solution.
Which Google foundation model should they use?
Antwort: A
Begründung:
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.
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48. Frage
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?
Antwort: D
Begründung:
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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49. Frage
A global news company is using a large language model to automatically generate summaries of news articles for their website. The model's summary of an international summit was accurate until it hallucinated by stating a detail that did not occur. How should the company overcome this hallucination?
Antwort: C
Begründung:
The core problem is the model's hallucination-it invented a factual detail-in a context (news reporting) where factual accuracy is non-negotiable. To correct a factual error in a generative summary, the model must be constrained to speak only based on verifiable facts from a reliable source.
The most effective technique to combat hallucinations and ensure factual adherence is Grounding (D). Grounding connects the Large Language Model's (LLM's) output to a specific, trusted, and verifiable source of information. This is often implemented using Retrieval-Augmented Generation (RAG). In this scenario, grounding the summary model on the original source articles ensures that every generated statement is directly entailed by the provided facts (the source article content).
Option B, fine-tuning, is expensive and only updates the model's general knowledge and style; it does not prevent the model from guessing or fabricating details when retrieving information. Option C, increasing temperature, would make the output less consistent and more diverse, likely increasing the chance of hallucination, which is the opposite of the desired effect. Option A is unrelated to factual accuracy. Therefore, Grounding is the necessary step to anchor the model's responses to the true content of the source articles.
(Reference: Google Cloud documentation on RAG/Grounding emphasizes that its primary purpose is to address the "knowledge cutoff" and hallucination issues of LLMs by retrieving relevant, up-to-date information from external knowledge sources and using this retrieved information to ground the LLM's generation, ensuring factual accuracy.)
50. Frage
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?
Antwort: D
Begründung:
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.
51. Frage
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