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Google Generative-AI-Leader 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
トピック 2	Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.
トピック 3	Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.
トピック 4	Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.

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Google Cloud Certified - Generative AI Leader Exam 認定 Generative-AI-Leader 試験問題 (Q24-Q29):

質問 # 24
A team is using a generative AI model to automatically generate short summaries of customer feedback. They need to ensure that these summaries are concise and easy to digest. What model setting should they adjust?

A. Output length
B. Temperature
C. Top-p (nucleus sampling)
D. Safety settings

正解：A

解説：
The objective is to make the generated summaries concise--that is, to control their length. In the configuration of a generative AI model, particularly a large language model (LLM), the parameter used to directly control the maximum size of the response is the Output Length parameter (often referred to as max_output_tokens or max_tokens). By setting a low limit on this parameter, the team can ensure that the model is forced to terminate its response once that limit is reached, resulting in a shorter, more concise summary that is "easy to digest," as requested.

質問 # 25
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. Reinforcement learning agent
B. Task-specific model
C. Vertex AI Model Garden
D. Foundation model

正解：D

解説：
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.

質問 # 26
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 filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.
B. To control the creativity and randomness of the model's output by adjusting the diversity of word choices.
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：A

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.

質問 # 27
A marketing team wants to use a foundation model to create social media and advertising campaigns. They want to create written articles and images from text. They lack deep AI expertise and need a versatile solution. Which Google foundation model should they use?

A. Veo
B. Gemma
C. Imagen
D. Gemini

正解：D

解説：
Gemini is Google's most advanced and multimodal foundation model, capable of understanding and generating various forms of content, including text and images, from a single prompt. Its versatility makes it suitable for marketing teams that need to create diverse campaign materials without deep AI expertise. Imagen is specifically for image generation, Gemma is a family of smaller, open models, and Veo is for video generation.
________________________________________

質問 # 28
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 cannot integrate with required financial databases.
B. Gemini deployment for this scenario would be too expensive and complex.
C. Gemini is designed for flexible content generation and inference, not rigid rule-based decisions.
D. Gemini is not equipped to handle structured numerical data for financial assessments.

正解：C

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 # 29
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