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Google Generative-AI-Leader Exam Syllabus Topics:

Section	Weight	Objectives
Business strategies for a successful gen AI solution	15%	- 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. Choosing the right model 2. Building a business case 3. Evaluating AI solutions - Describe change management best practices and their importance. 1. Creating a culture of innovation 2. Enabling AI adoption
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 prompt engineering techniques and their purpose. 1. Zero-shot 2. One-shot 3. Chain of thought 4. Few-shot - Describe how grounding can be used to improve model output. 1. Grounding with Google Search 2. Grounding with enterprise data
Google Cloud's generative AI offerings	35%	- Identify the use cases and strengths of Google's foundation models. 1. Gemini 2. Imagen 3. Veo 4. Gemma - Describe Google Cloud's gen AI product and service portfolio. 1. Gemini for Google Cloud 2. Google Workspace 3. Vertex AI 4. Vertex AI Studio 5. Model Garden
Fundamentals of generative AI	30%	- Describe core generative AI (gen AI) concepts and use cases. 1. 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 2. 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) 3. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost) 4. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement) - 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 - Identify the core layers of the gen AI landscape and the business implications. 1. Models 2. Applications 3. Infrastructure 4. Agents 5. Platforms

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Generative-AI-Leader日本語版試験解答、Generative-AI-Leader関連問題資料

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Google Cloud Certified - Generative AI Leader Exam 認定 Generative-AI-Leader 試験問題 (Q116-Q121):

質問 # 116
A sales manager wants to responsibly use generative AI (gen AI) to increase efficiency with their existing tasks. They want to allow the sales team to focus on building customer relationships and closing deals. How should the sales team use gen AI?

A. To automate creative content like blog posts and social media updates to attract new leads.
B. To replace the sales team's CRM system with a more intuitive and user-friendly interface.
C. To analyze customer interactions on social media and automatically generate sales pitches tailored to their public profiles.
D. To draft emails and provide real-time insights about customer needs.

正解：D

解説：
The strategic goal is to boost sales efficiency by shifting the team's focus to high-value activities (relationships and closing deals) by automating repetitive administrative tasks.
Option C directly addresses this goal by leveraging Gen AI's core capabilities for text generation and summarization/analysis:
Drafting emails automates a major time sink for sales reps (a common, repetitive task).
Providing real-time insights automates the labor-intensive research and manual data analysis required to understand customer needs, giving the rep instant, actionable context.
Options A and D are less direct solutions for improving sales efficiency: Option A is an expensive, high-risk platform replacement, not an efficiency use case. Option D describes marketing tasks, which, while related, are not the primary, day-to-day tasks that sales reps perform to clear their schedules for relationship building. Therefore, Gen AI's most effective role in sales is as a productivity assistant for drafting and quick research.
(Reference: Google Cloud documentation on sales enablement use cases emphasizes that Gen AI's role is to automate administrative and time-consuming tasks like drafting outreach messages and synthesizing customer information to enhance seller productivity, allowing them to focus on revenue-generating activities.)

質問 # 117
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively.
What machine learning should the company use?

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

正解：A

質問 # 118
A manager wants to ensure that only quality data is used in their AI model. Which scenario is most likely to lead to an unfair and biased outcome?

A. A financial institution builds a fraud detection model using transaction data that is incomplete, lacking details about the merchants involved in a significant percentage of the transactions.
B. A technology firm trains a facial recognition system primarily using images of individuals from one demographic group, with limited representation from other groups.
C. A company's customer service natural language processing model is trained on a dataset containing incorrectly encoded text from older website versions.
D. A marketing team creates a personalized recommendation engine based on website clickstream data that includes bot traffic.

正解：B

解説：
Training a facial-recognition system predominantly on one demographic creates representation bias. The model receives insufficient examples from other groups and will probably perform less accurately for those populations, producing systematically unequal outcomes. This is directly associated with fairness because model performance varies according to demographic characteristics. The other scenarios describe serious data-quality problems, but their primary effects differ. Missing merchant details can reduce fraud-detection accuracy, incorrectly encoded text introduces corruption, and bot traffic distorts customer-behavior signals. Those defects may degrade overall performance without necessarily disadvantaging a protected or underrepresented group. Responsible AI development requires representative datasets, subgroup-level evaluation, documented data provenance, and ongoing monitoring for unequal error rates. Therefore, the facial-recognition dataset presents the clearest and most direct risk of an unfair and biased outcome.

質問 # 119
A highly regulated financial institution wants to use Gemini as the core decision engine for a loan approval system that will deterministically approve or reject loan applications based on a strict set of predefined criteria. Why is this an inappropriate use case for Gemini?

A. Gemini is designed for flexible content generation and inference, not rigid rule-based decisions.
B. Gemini is not equipped to handle structured numerical data for financial assessments.
C. Gemini deployment for this scenario would be too expensive and complex.
D. Gemini cannot integrate with required financial databases.

正解：A

解説：
Gemini, as a large language model, excels at flexible content generation, summarization, understanding, and inference. However, it is not designed for deterministic, rule-based decision- making that requires absolute consistency and adherence to strict, predefined criteria, as is common in highly regulated financial systems like loan approvals. Such systems typically require traditional programming logic or specific rule engines for auditable and consistent outcomes.

質問 # 120
A company is using a generative AI model to personalize marketing content for its customers. Customer data used to train the model includes customer names and purchase histories. They want to balance the benefits of personalization with the need to protect customer privacy. What technique should they use when processing this data?

A. Using pseudonymization to replace direct identifiers with artificial identifiers.
B. Storing the customer data on secure, encrypted servers.
C. Regularly auditing the AI model's outputs to ensure they are not discriminatory.
D. Obtaining explicit consent from customers to use their data in AI models.

正解：A

解説：
Pseudonymization is the data de-identification technique that replaces direct personally identifiable information (PII), such as customer names or account IDs, with artificial identifiers (tokens or pseudonyms). This preserves the underlying behavioral and relational utility of the transaction data needed for personalization algorithms while preventing direct exposure or memorization of customer identities during training and model processing.

質問 # 121
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