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Google Generative-AI-Leader Exam Syllabus Topics:

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

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Google Cloud Certified - Generative AI Leader Exam 認定 Generative-AI-Leader 試験問題 (Q40-Q45):

質問 # 40
A company is developing a generative AI application to analyze customer feedback collected through online surveys. Stakeholders are concerned about potential privacy risks associated with this data, as the feedback contains personally identifiable information (PII). They need to mitigate these risks before using the data to train the AI model. What action should the company prioritize?

  • A. Ensuring that the AI model is trained on a large and diverse dataset.
  • B. Implementing strong access controls to limit which teams can view the raw survey data.
  • C. Applying data anonymization techniques to remove or obscure sensitive data.
  • D. Focusing on collecting only quantitative feedback data in future surveys.

正解：C

解説：
The problem is the existence of Personally Identifiable Information (PII) within the customer feedback data, which introduces privacy risks for the development and training of the generative AI model. The goal is to mitigate these risks before using the data to train the AI model.
According to Google ' s Responsible AI and data handling best practices, when sensitive data like PII is present in a dataset intended for model training, the most critical step to prioritize is data minimization and privacy protection at the source. This is often achieved through anonymization or de-identification.
Applying data anonymization techniques (D) directly addresses the risk by removing or obscuring the sensitive data elements. This prevents the PII from being embedded into the model ' s parameters during training, thereby eliminating the risk of data leakage or privacy violations in the AI application ' s outputs.
This is a crucial early step in the ML lifecycle for datasets containing sensitive information.
Option C, implementing access controls, is a necessary security measure but is a reactive control that protects the raw data; it does not remove the PII risk from the derived model itself. Option A is a long-term change to data collection but doesn ' t solve the problem for the existing data. Option B relates to bias and accuracy, not specifically PII risk mitigation.
(Reference: Google Cloud ' s Secure AI Framework (SAIF) and Responsible AI principles emphasize protecting sensitive data at all stages of the ML lifecycle, with de-identification being the primary method before training.)


質問 # 41
A company ' s sales team spends a significant amount of time researching potential leads and manually entering data into their customer relationship management (CRM) tool. They want to improve the team ' s efficiency and enable them to focus on building relationships and closing deals. What should the organization do?

  • A. Integrate the CRM with a popular sales intelligence platform to automatically enrich lead profiles with valuable data.
  • B. Develop a custom AI solution using Google Cloud's AutoML Natural Language to analyze lead communications and automatically update the CRM.
  • C. Implement Gemini Enterprise " unified enterprise search " including a CRM agent to automate lead research and data entry.
  • D. Implement Google Cloud ' s Contact Center AI to qualify leads and route them to the appropriate sales representatives.

正解：C

解説：
The core objective is to automate two distinct administrative pain points for the sales team: lead research and manual data entry into the CRM , allowing them to prioritize relationship-building.
Implementing Gemini Enterprise unified enterprise search including a CRM agent (C) directly solves this problem. Gemini Enterprise provides multi-source connectors that pull data across siloed corporate repositories, creating a " unified enterprise search " environment. By attaching a specialized CRM agent to this ecosystem, the agent can use internal and external tools to automatically research lead background information, synthesize the findings, and interact directly with the CRM ' s APIs to update lead profiles without manual human data entry.
* Option A relies on traditional static sales databases which lack the dynamic reasoning and research automation of a Gen AI agent.
* Option B suggests AutoML Natural Language , which is a traditional discriminative ML tool for text classification or entity extraction; it cannot perform autonomous multi-step research or execute actions like an agent.
* Option D, Contact Center AI , is designed for handling live customer telephone or chat interactions, not background lead research and CRM data entry.
(Reference: Google Cloud documentation on Gemini Enterprise and workspace agent frameworks outlines how unified search capabilities and workflow agents connect internal systems like CRMs to cross-reference data, automatically research background details, and eliminate manual data entry workloads.)


質問 # 42
A company wants to build a model to classify customer reviews as positive, negative, or neutral. They have collected a dataset of thousands of customer reviews, and each review has been manually tagged with the corresponding sentiment: positive, negative, or neutral. What machine learning should the company use?

  • A. Unsupervised learning
  • B. Reinforcement learning
  • C. Deep learning
  • D. Supervised learning

正解：D

解説：
The machine learning approach is determined by the nature of the data available and the desired output.
Data Available: Customer reviews (input) that are manually tagged with a sentiment category (output/label).
Desired Output: A model that can classify new, untagged reviews into one of the predefined categories (positive, negative, or neutral).
This scenario perfectly aligns with the definition of Supervised Learning (D). Supervised learning is the machine learning paradigm where the model is trained on a labeled dataset-a dataset where the input data is explicitly paired with the correct output label. The model learns a function that maps the input (the review text) to the output (the sentiment tag) and is then used to predict the label for unseen data.
Unsupervised Learning (B) is used for unlabeled data to find hidden patterns or groupings (clustering), which is not the goal here.
Reinforcement Learning (C) is used for training an agent through trial and error using a system of rewards and penalties.
Deep Learning (A) is a type of model (using deep neural networks) that can be used for supervised learning, but the learning approach required here is definitively supervised.
(Reference: Google's training materials on Machine Learning Approaches define Supervised Learning as training a model using labeled data to make predictions or classifications for new, unseen inputs. Sentiment analysis is a canonical example of a supervised learning classification task.)


質問 # 43
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 provide the computational resources needed to train and run generative AI models.
  • C. To allow access to pre-trained gen AI models.
  • D. To provide tools for users to interact with gen AI models, and build and deploy AI solutions.

正解：D


質問 # 44
A user asks a generative AI model about the scientific accuracy of a popular science fiction movie. The model confidently states that humans can indeed travel faster than light, referencing specific but entirely fictional theories and providing made-up explanations of how this is achieved according to the movie's "established science." The model presents this information as factual, without indicating that it originates from a fictional work. What type of model limitation is this?

  • A. Knowledge cutoff
  • B. Bias
  • C. Hallucination
  • D. Data dependency

正解：C

解説：
The limitation described is the AI model generating a false or misleading response (humans traveling faster than light is scientifically impossible/unproven) and presenting it as fact (confidently stating a fictional theory is real) without the ability to indicate its uncertainty or the source's fictional nature. This is the definition of a Hallucination in generative AI.
AI Hallucinations occur when a Large Language Model (LLM) generates outputs that are factually incorrect, irrelevant, or nonsensical, despite being linguistically fluent and seemingly plausible. They arise because the model is designed to predict the most statistically probable next word or token based on its training data, even when it lacks information or when its training data contains a mixture of fact and fiction. The model is overconfident in its generated response, a behavior that diminishes user trust and reliability, especially in applications where factual accuracy is critical. While a knowledge cutoff (B) is a common cause of hallucinations when an LLM is asked about recent events, the core limitation of fabricating facts from its own hardwired knowledge is the hallucination itself. Data dependency (A) relates to the model's reliance on the quality and completeness of its training data, and while flawed training data can be a cause, the error mode of inventing facts is the Hallucination.


質問 # 45
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