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Google Generative-AI-Leader Exam Syllabus Topics:

Section	Weight	Objectives
Fundamentals of generative AI	30%	- Describe how various data types are used in gen AI and the business implications. 1. Identifying the differences between structured and unstructured data, and identifying real world examples of each type 2. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format) 3. Identifying the differences between labeled and unlabeled data - Describe core generative AI (gen AI) concepts and use cases. 1. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement) 2. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost) 3. Identifying the stages of the machine learning lifecycle (e.g., data ingestion, data preparation, model training, model deployment, model management) and the Google Cloud tools for each stage 4. Defining core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models) - Identify the core layers of the gen AI landscape and the business implications. 1. Infrastructure 2. Models 3. Platforms 4. Applications 5. Agents
Business strategies for a successful gen AI solution	15%	- Describe best practices for a successful gen AI project. 1. Choosing the right model 2. Evaluating AI solutions 3. Building a business case - Describe Google's approach to responsible AI and its importance. 1. Google's AI principles 2. Responsible AI best practices - Describe change management best practices and their importance. 1. Enabling AI adoption 2. Creating a culture of innovation
Techniques to improve gen AI model output	20%	- Describe the process of fine-tuning gen AI models. 1. Reinforcement learning from human feedback (RLHF) 2. Supervised tuning - Describe how grounding can be used to improve model output. 1. Grounding with enterprise data 2. Grounding with Google Search - Describe prompt engineering techniques and their purpose. 1. Few-shot 2. Chain of thought 3. One-shot 4. Zero-shot
Google Cloud's generative AI offerings	35%	- Describe Google Cloud's gen AI product and service portfolio. 1. Vertex AI Studio 2. Model Garden 3. Google Workspace 4. Gemini for Google Cloud 5. Vertex AI - Identify the use cases and strengths of Google's foundation models. 1. Gemini 2. Imagen 3. Veo 4. Gemma

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q85-Q90):

NEW QUESTION # 85
What is the function of the platform layer in the generative AI (gen AI) landscape?

A. To allow users to interact with and leverage AI capabilities.
B. To allow access to pre-trained gen AI models.
C. To provide the computational resources needed to train and run generative AI models.
D. To provide tools for users to interact with gen AI models, and build and deploy AI solutions.

Answer: D

NEW QUESTION # 86
A company wants to adopt generative AI and is concerned about vendor lock-in. They want to maintain flexibility in their technology stack. What Google Cloud strength would ease their concerns?

A. Google Cloud's strict adherence to proprietary technologies ensures the highest level of security and performance.
B. Google Cloud's AI solutions have an open approach that supports customer choice across offerings.
C. Google Cloud's focus on automation aims to replace human jobs with AI systems, potentially leading to significant workforce reductions.
D. Google Cloud's AI solutions are pre-packaged for easy deployment, eliminating the need for customization and integration efforts.

Answer: B

Explanation:
Google Cloud promotes an open and flexible approach to its AI offerings, supporting open standards, open- source initiatives (like TensorFlow, Kubernetes, and Gemma), and providing various integration options. This helps alleviate vendor lock-in concerns by giving customers choice and control over their technology stack.
________________________________________

NEW QUESTION # 87
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?

A. Use prompt chaining.
B. Use few-shot prompting.
C. Use role prompting.
D. Adjust the temperature parameter.

Answer: C

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.
________________________________________

NEW QUESTION # 88
A company needs a versatile AI model for tasks like drafting emails, summarizing documents, generating images, and assisting with code to improve efficiency across departments. What is the main advantage of using Gemini for this use case?

A. Specialized data analysis for business intelligence, even for non-technical users.
B. The ability to easily create highly customized AI agents to automate complex tasks with minimal input.
C. Being completely open-source for modification without licensing costs.
D. The ability to handle text, images, and code to support a wide range of tasks.

Answer: D

Explanation:
Gemini's principal advantage in this scenario is its multimodal capability. Gemini models can understand and generate content across multiple formats, including text, images, and code. This versatility allows one model family to support email drafting, document summarization, image generation workflows, and software- development assistance across different departments. Gemini is not completely open-source, eliminating option A. Although Gemini can support the creation of AI agents, agent customization is not the defining benefit connecting all the tasks described. Specialized business-intelligence analysis is also narrower than the organization's cross-functional requirements. The question emphasizes several different content types and activities rather than one specialized workflow. Therefore, Gemini's ability to work with text, visual information, and code provides the broadest and most directly relevant advantage for improving organizational efficiency.

NEW QUESTION # 89
At mcnz.com your AI team wants one versatile model that they can prompt or fine tune to handle text generation, multilingual translation, and question answering across 18 languages for three product lines. What is the term for a large pretrained model that serves as a general purpose starting point for many downstream applications?

A. Vertex AI Model Garden
B. Foundation model
C. Reinforcement learning agent
D. Task-specific model

Answer: B

Explanation:
A foundation model is a large pretrained model that serves as a general purpose starting point that you can adapt through prompting or fine tuning for many downstream applications. It is intended to handle varied natural language tasks such as text generation, multilingual translation, and question answering across many languages. This versatility matches the team's requirement for one model that supports multiple product lines and tasks.

NEW QUESTION # 90
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