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Google Generative-AI-Leader Exam Syllabus Topics:

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

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Google Cloud Certified - Generative AI Leader Exam 認定 Generative-AI-Leader 試験問題 (Q36-Q41):

質問 # 36
A company is evaluating different generative AI (gen AI) platforms and wants to understand the role of the infrastructure layer in supporting the development and deployment of gen AI models. What is the function of the infrastructure layer in the gen AI landscape?

A. To provide access to pre-trained gen AI models.
B. To provide the compute resources needed to run and train AI models and store training data.
C. To provide a user-friendly interface for interacting with gen AI models.
D. To provide tools and resources for building and deploying gen AI applications.

正解：B

解説：
The infrastructure layer supplies the foundational computing, storage, networking, and acceleration resources required to train and run generative AI models. This includes CPUs, GPUs, TPUs, high-performance networks, scalable storage, and systems optimized for demanding AI workloads. Training foundation models and serving model responses require substantial processing capacity, while training datasets and model artifacts require reliable storage. Access to pre-trained models belongs primarily to the model layer. A user- friendly model interface is part of the application or experience layer. Development, deployment, tuning, and management tools belong to the platform layer. These layers work together, but their functions are distinct.
Because the question asks specifically about the infrastructure layer, the correct function is supplying the computational resources and data storage needed to train and operate AI models.

質問 # 37
A company is trying to decide which platform to use to optimize its generative AI (gen AI) solutions.
Why should the company use Vertex AI Platform?

A. It provides a unified platform of tools for building, deploying, and managing machine learning.
B. It provides a mechanism for efficient analysis and exploration of large datasets used in machine learning.
C. It provides gen AI coding assistance with enterprise security and privacy protection.
D. It provides scalable and cost-effective object storage for data used in machine learning workflows.

正解：A

解説：
Vertex AI is Google Cloud's core, end-to-end Machine Learning Operations (MLOps) platform, designed to cover the entire ML lifecycle.
The key benefit of Vertex AI, particularly for generative AI, is that it provides a unified platform (D) where all stages of AI development--from accessing foundation models in Model Garden, testing in Vertex AI Studio, training and tuning (via tools like Reinforcement Learning from Human Feedback), to deploying, and monitoring models in production--can be managed from a single service. This significantly reduces complexity, improves collaboration between teams (data scientists, engineers, business leaders), and ensures enterprise-grade governance and scalability necessary for production Gen AI solutions.

質問 # 38
SummitCart, a global e commerce fulfillment company, is deploying a generative AI driven system in its regional distribution centers to observe conveyor operations and forecast sorter and motor failures in real time. Any outage would pause order packing and could cost several million dollars per hour. When choosing the model and the managed platform, which characteristic should be prioritized for this mission critical rollout?

A. Access to cutting edge features before general availability
B. The shortest end to end response latency
C. The lowest per request pricing across regions
D. High availability with a firm uptime Service Level Agreement

正解：D

解説：
This rollout is mission critical and any downtime would incur enormous costs, so the platform and model selection must prioritize guaranteed uptime.
For an always-on operational system in distribution centers you need high availability commitments that are explicit and enforceable. A documented uptime SLA signals that the provider designs and operates the service for reliability and that it will be supported with measurable objectives and remediation if targets are missed. Choosing services that publish clear availability targets and provide regional resilience, failover capabilities, and enterprise support reduces the risk of production outages and protects revenue.

質問 # 39
A development team is configuring a generative AI model for a customer-facing application and wants to ensure the generated content is appropriate and harmless. What is the primary function of the safety settings parameter in a generative AI model?

A. To control the creativity and randomness of the model's output by adjusting the diversity of word choices.
B. To filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.
C. To limit the maximum text length that the model generates by ensuring concise responses.
D. To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.

正解：B

解説：
Safety settings in generative AI models are specifically designed to prevent the generation of content that could be harmful, offensive, or inappropriate. This includes filtering for categories like hate speech, sexually explicit content, self-harm, and violence, based on predefined thresholds. Options A, B, and D refer to other parameters like max_output_tokens or temperature, which control output length, input/output processing, and creativity, respectively, not safety.

質問 # 40
A global news company is using a large language model to automatically generate summaries of news articles for their website. The model's summary of an international summit was accurate until it hallucinated by stating a detail that did not occur. How should the company overcome this hallucination?

A. Fine-tune the model on a larger dataset of news articles.
B. Use grounding to base the model output on the source articles.
C. Increase the temperature setting of the model to encourage more diverse outputs.
D. Implement stricter safety settings to filter out potentially controversial topics.

正解：B

解説：
The core problem is the model's hallucination--it invented a factual detail--in a context (news reporting) where factual accuracy is non-negotiable. To correct a factual error in a generative summary, the model must be constrained to speak only based on verifiable facts from a reliable source.
The most effective technique to combat hallucinations and ensure factual adherence is Grounding (D). Grounding connects the Large Language Model's (LLM's) output to a specific, trusted, and verifiable source of information. This is often implemented using Retrieval- Augmented Generation (RAG). In this scenario, grounding the summary model on the original source articles ensures that every generated statement is directly entailed by the provided facts (the source article content).

質問 # 41
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