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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Fundamentals of generative AI30%- Describe how various data types are used in gen AI and the business implications.
  • 1. Identifying the differences between labeled and unlabeled data
  • 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 structured and unstructured data, and identifying real world examples of each type
- Describe core generative AI (gen AI) concepts and use cases.
  • 1. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost)
  • 2. 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
  • 3. 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)
  • 4. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement)
- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Platforms
  • 2. Applications
  • 3. Agents
  • 4. Infrastructure
  • 5. Models
Topic 2: Techniques to improve gen AI model output20%- Describe the process of fine-tuning gen AI models.
  • 1. Reinforcement learning from human feedback (RLHF)
  • 2. Supervised tuning
- Describe prompt engineering techniques and their purpose.
  • 1. Zero-shot
  • 2. One-shot
  • 3. Few-shot
  • 4. Chain of thought
- Describe how grounding can be used to improve model output.
  • 1. Grounding with Google Search
  • 2. Grounding with enterprise data
Topic 3: Google Cloud's generative AI offerings35%- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Gemini for Google Cloud
  • 2. Model Garden
  • 3. Vertex AI Studio
  • 4. Vertex AI
  • 5. Google Workspace
- Identify the use cases and strengths of Google's foundation models.
  • 1. Imagen
  • 2. Veo
  • 3. Gemma
  • 4. Gemini
Topic 4: Business strategies for a successful gen AI solution15%- Describe best practices for a successful gen AI project.
  • 1. Evaluating AI solutions
  • 2. Building a business case
  • 3. Choosing the right model
- Describe change management best practices and their importance.
  • 1. Creating a culture of innovation
  • 2. Enabling AI adoption
- Describe Google's approach to responsible AI and its importance.
  • 1. Responsible AI best practices
  • 2. Google's AI principles

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Google Cloud Certified - Generative AI Leader Exam 認定 Generative-AI-Leader 試験問題 (Q66-Q71):

質問 # 66
An organization wants to understand trends in customer interactions, identify common issues, gauge customer sentiment, and improve the overall customer experience across both their automated chatbot interactions and live agent support. They need a tool that can analyze their existing conversational data to gain actionable business intelligence. What component of Google's Customer Engagement Suite best addresses this need?

正解：B

解説：
The requirement is clearly focused on analytics and business intelligence derived from existing conversational data, specifically to understand trends and sentiment. Conversational Insights is the dedicated component within Google's Customer Engagement Suite (which includes Contact Center AI) whose primary function is to analyze large volumes of interaction data (transcripts from chat, calls, etc.). It uses AI and Natural Language Processing (NLP) to extract valuable patterns, identify root causes of issues, and measure customer sentiment and agent performance. This analysis generates the actionable insights necessary for strategic planning and overall customer experience improvement.


質問 # 67
An organization wants granular control over who can use and see their generative AI models and related resources on Google Cloud. Which Google Cloud security offering is specifically for this purpose?

正解：B

解説：
Identity and Access Management (IAM) is the fundamental Google Cloud service that allows you to define who has what access to which resources. It provides granular control over permissions for users, groups, and service accounts, including access to generative AI models and related data.
________________________________________


質問 # 68
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?

正解：B

解説：
Given the tasks


質問 # 69
According to Google-recommended practices, when should generative AI be used to automate tasks?

正解：C

解説：
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).


質問 # 70
A company wants to create an AI-powered educational solution that provides personalized learning experiences for students. This platform will assess a student's knowledge, recommend relevant learning materials, and generate personalized exercises. The application would provide the structure for lessons and track progress. What type of AI solution should they use?

正解：D

解説：
The request goes beyond just recommendations or content generation. It involves assessing knowledge, recommending materials, generating personalized exercises, providing lesson structure, and tracking progress.
This implies a more comprehensive, intelligent system that acts as an assistant or tutor for the student, which is best described as a customized learning agent. This agent would likely leverage LLMs and recommendation systems as components, but the overall solution is an agent.
________________________________________


質問 # 71
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