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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of Generative AI | 30% | - Core concepts and characteristics of generative AI - Foundation models: definition, capabilities, and use cases - Key technologies and differences from traditional AI - Responsible AI principles and application |
| Business Strategies for Successful Generative AI Solutions | 15% | - Scaling and measuring success of generative AI initiatives - Planning and adoption frameworks - Governance, risk management, and compliance - Identifying business use cases and value opportunities |
| Techniques to Improve Generative AI Model Output | 20% | - Prompt engineering principles and best practices - Fine-tuning and adaptation methods - Mitigation of bias and inaccuracies - Evaluation and optimization of output quality |
| Google Cloud's Generative AI Offerings | 35% | - Generative AI application development platforms - Enterprise integration and security features - Model Garden and available models - Overview of Google Cloud generative AI services and tools - Vertex AI generative AI capabilities |
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NEW QUESTION # 82
What is a characteristic of Google Cloud as a generative AI company?
Answer: D
Explanation:
Google Cloud emphasizes an AI-first approach, integrating AI capabilities across its services and consistently innovating with new models and features. While security is a high priority, fully autonomous AI agents requiring zero configuration are generally not the norm, and "completely secured and isolated from external networks" is an oversimplification of cloud security models.
Google also contributes to and supports open-source AI initiatives, not solely relying on proprietary closed-source technologies.
NEW QUESTION # 83
What is an example of unsupervised machine learning?
Answer: D
Explanation:
Unsupervised learning deals with unlabeled data. Identifying "natural groupings" or clusters in customer purchase patterns (e.g., segmenting customers into different buying behaviors without pre-defined labels) is a classic example of unsupervised learning (clustering). Options B, C, and D are examples of supervised learning, as they involve labeled data for training (product categories, renewal status, sales figures).
NEW QUESTION # 84
A nationwide retail chain plans to retire its aging on premises contact center stack and move to a cloud first model that uses AI throughout customer interactions. The company requires a single enterprise ready foundation that unifies telephony, IVR, conversational virtual agents, and real time agent assist features while scaling globally as call volumes grow. Which Google Cloud solution best fits this fully managed end to end contact center platform need?
Answer: A
Explanation:
This fully managed solution provides a single enterprise ready foundation that unifies telephony, IVR, conversational virtual agents built with Dialogflow CX, and real time Agent Assist. It is designed to scale globally as call volumes grow and to deliver reliability, security, and compliance while reducing the need to stitch together multiple products.
NEW QUESTION # 85
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?
Answer: A
Explanation:
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.)
NEW QUESTION # 86
What does Model Garden enable a company to do?
Answer: D
Explanation:
Model Garden is a key component of the Vertex AI Platform on Google Cloud, positioned as an AI/ML model library. Its core function is to provide a central, organized place for users to find and utilize a wide variety of machine learning assets.
Specifically, Model Garden enables customers to:
Discover a curated collection of models, including Google's latest Foundation Models (like Gemini and Imagen), specialized models, and enterprise-ready models from Google partners and the open-source community (e.g., Gemma).
Test and customize these models, often with tools like Vertex AI Studio for prompt tuning or fine-tuning with custom data.
Deploy the selected and customized models directly to applications with a consistent deployment pattern.
Options B and C describe features of other MLOps tools within Vertex AI (Model Evaluation and Model Registry/Metadata Management). Option D describes the Custom Training service within Vertex AI. Model Garden's unique value proposition is acting as the starting point: a marketplace or repository to discover and immediately deploy or customize existing, pre-trained models.
(Reference: Google Cloud documentation states that Model Garden on Vertex AI is a place to discover, test, customize, and deploy a wide variety of models from Google and Google partners, including first-party and open-source models.)
NEW QUESTION # 87
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