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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of Generative AI | 30% | - Key technologies and differences from traditional AI - Foundation models: definition, capabilities, and use cases - Core concepts and characteristics of generative AI - Responsible AI principles and application |
| Topic 2: Google Cloud's Generative AI Offerings | 35% | - Model Garden and available models - Enterprise integration and security features - Overview of Google Cloud generative AI services and tools - Vertex AI generative AI capabilities - Generative AI application development platforms |
| Topic 3: Business Strategies for Successful Generative AI Solutions | 15% | - Identifying business use cases and value opportunities - Planning and adoption frameworks - Scaling and measuring success of generative AI initiatives - Governance, risk management, and compliance |
| Topic 4: Techniques to Improve Generative AI Model Output | 20% | - Evaluation and optimization of output quality - Mitigation of bias and inaccuracies - Prompt engineering principles and best practices - Fine-tuning and adaptation methods |
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NEW QUESTION # 41
An organization is deploying generative AI applications on Google Cloud. They want to understand the fundamental security measures integrated into Google ' s global network and hardware. What term specifically describes these inherent protective measures?
Answer: C
Explanation:
Secure-by-design infrastructure means that security protections are incorporated into Google Cloud's hardware, network, and software architecture from their initial design rather than being added only after deployment. Google's infrastructure applies layered controls such as custom hardware security, encryption, identity-based access, network protections, continuous monitoring, and a hardened global architecture. These measures protect workloads and data at multiple levels and provide a secure foundation for generative AI applications. Network protection describes only one part of this broader architecture. Operations management concerns administration and reliability processes, while security oversight refers generally to governance and supervision rather than inherent technical controls. Because the organization is asking for the term covering security measures fundamentally integrated into Google's global network and hardware, secure-by-design infrastructure is the precise and comprehensive answer.
NEW QUESTION # 42
According to Google-recommended practices, when should generative AI be used to automate tasks?
Answer: A
Explanation:
The strategic value of Generative AI (Gen AI) in a business context, as taught in Google's courses, is primarily to enhance efficiency and productivity by taking over tasks that consume significant employee time.
Gen AI excels in automating tasks that:
Are repetitive and time-consuming, such as drafting initial emails, summarizing long documents, or generating code snippets. Automating these routine tasks (C) frees employees to focus on higher-value activities (like building customer relationships or strategic planning).
Involve the generation of new content based on patterns learned from large datasets (e.g., text, images, code).
Options A and D represent high-value, strategic work-highly creative or complex strategic decision-making-where human judgment and oversight remain paramount. While Gen AI can assist with these (e.g., brainstorming creative ideas or providing data-backed insights), it is generally not recommended for full automation. Option B explicitly requires human oversight due to its sensitive nature. Therefore, the best fit for full or augmented automation for efficiency is the handling of routine, repeatable, and non-complex tasks.
(Reference: Google Cloud documentation on Gen AI adoption and efficiency states that Gen AI transforms work by automating repetitive and time-consuming tasks to free up time for strategic thinking and creativity.)
NEW QUESTION # 43
What is an example of supervised machine learning?
Answer: B
Explanation:
Supervised machine learning trains a model using examples containing both input features and known output labels. Historical support tickets can include characteristics such as issue category, priority, customer type, and complexity, together with the actual resolution time. The model learns the relationship between those labeled examples and then predicts resolution time for new tickets. The remaining scenarios focus on discovering previously unknown structures in unlabeled data. Identifying customer segments from purchase history and finding behavior clusters from website clicks are clustering tasks. Automatically discovering recurring topics in customer reviews is generally a topic-modeling task. These are examples of unsupervised learning because the system is not trained against predetermined target outcomes. Therefore, predicting resolution time from past support tickets with known resolution-time labels is the clear example of supervised machine learning.
NEW QUESTION # 44
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 # 45
An organization is building a generative AI agent for employee travel bookings. The agent needs to connect to external flight and hotel systems for availability, pricing, and reservations. They implement the most simple and effective way for the agent to interact with the external travel provider system, following Google Cloud- recommended practices. What method should they use?
Answer: D
Explanation:
Extensions provide a standardized mechanism through which an AI agent can invoke external APIs and complete real-world actions. A travel extension can connect the agent to flight and hotel services, pass structured parameters, retrieve current availability and prices, and submit reservations. This is more maintainable and consistent than embedding separate custom functions directly inside the agent. Pre-loaded data stores cannot satisfy the requirement because availability and pricing change continuously, while reservations require transactional access to the provider's live system. "Plugins" is not the recommended Google Cloud mechanism identified for this agent-integration scenario. Extensions enable agents to move beyond answering questions by securely communicating with external services through defined interfaces.
Consequently, they are the simplest and most effective Google-recommended method for integrating the travel agent with provider APIs.
NEW QUESTION # 46
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