Generative-AI-Leader実際試験 & Generative-AI-Leader復習問題集

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Google Generative-AI-Leader Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Techniques to improve gen AI model output	20%	- Describe prompt engineering techniques and their purpose. 1. Chain of thought 2. Few-shot 3. One-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
Topic 2: Google Cloud's generative AI offerings	35%	- Identify the use cases and strengths of Google's foundation models. 1. Veo 2. Gemma 3. Imagen 4. Gemini - Describe Google Cloud's gen AI product and service portfolio. 1. Model Garden 2. Google Workspace 3. Vertex AI 4. Gemini for Google Cloud 5. Vertex AI Studio
Topic 3: Business strategies for a successful gen AI solution	15%	- Describe best practices for a successful gen AI project. 1. Evaluating AI solutions 2. Choosing the right model 3. Building a business case - 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
Topic 4: Fundamentals of generative AI	30%	- Identify the core layers of the gen AI landscape and the business implications. 1. Platforms 2. Models 3. Agents 4. Infrastructure 5. Applications - 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. 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. 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. 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

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Google Cloud Certified - Generative AI Leader Exam 認定 Generative-AI-Leader 試験問題 (Q11-Q16):

質問 # 11
A home loan company is deploying a generative AI system to automate initial loan application reviews.
Several applicants have been unexpectedly rejected, leading to customer complaints and potential bias concerns. They need to ensure responsible and fair lending practices. What aspect of the AI system should they prioritize?

A. Regularly updating the AI model with more financial data to improve its accuracy over time.
B. Ensuring AI decision-making is explainable to understand decision reasons and establish accountability.
C. Implementing stricter data security measures to protect applicants ' financial information from unauthorized access.
D. Increasing the speed at which the AI system processes loan applications to handle the high volume.

正解：B

解説：
The problem centers on unexpected rejections and potential bias in a high-stakes, regulated domain (lending).
In such a context, the central tenet of Responsible AI is transparency and fairness.
While all options are valid goals, the priority when facing bias concerns and customer complaints due to rejection is to provide accountability and verify the fairness of the automated decision. This is achieved through Explainable AI (XAI).
Ensuring AI decision-making is explainable (B) means building mechanisms that allow developers, regulators, and affected customers to understand why a specific decision (rejection) was made. Explainability is crucial for:
Auditing for bias: If the reasons for rejection can be traced (e.g., system rejects based on loan-to-value ratio, not race), bias can be identified and corrected.
Compliance: Financial services are heavily regulated, and the ability to explain a lending decision is often a legal or regulatory requirement.
Customer Trust: Providing a clear reason for rejection (even if the news is bad) reduces complaints and fosters confidence, directly addressing the core issue of unexpected rejections.
Options A, C, and D address security, speed, and accuracy, respectively, but Explainability is the direct mechanism for proving fairness and ensuring accountability, making it the most critical priority in this scenario.
(Reference: Google ' s Responsible AI principles and training materials highlight that in high-stakes domains like finance, explainability is essential for establishing trust, identifying and mitigating bias, and meeting regulatory compliance.)

質問 # 12
A boutique sneaker label plans to use generative AI to render concept images for upcoming footwear lines. They can choose between a compact and carefully curated set of about 15,000 images of their own past collections and a very large and mixed set of roughly 2.2 million generic footwear photos from public sources. If they prioritize the smaller curated set of their proprietary designs as the primary training data, what outcome should they expect from the resulting model?

A. Is far more likely to memorize and output near replicas of training images
B. Generates a much broader spectrum of entirely novel styles that do not resemble the label identity
C. More closely mirrors the brand signature style and design language even if overall variety may be reduced
D. Is cheaper to train yet may have difficulty learning the brand specific aesthetic

正解：C

解説：
Prioritizing a smaller but curated set of proprietary images makes the model learn the label's distinctive patterns, motifs, and color relationships more strongly. The training signal concentrates on consistent brand examples, so the generator better reproduces the brand's aesthetic and design language. Because the data distribution is narrower than a large mixed corpus, the model tends to explore fewer directions and may trade off breadth of styles for fidelity to the brand identity.

質問 # 13
A company is using a language model to solve complex customer service inquiries. For a particular issue, the prompt includes the following instructions:
"To address this customer's problem, we should first identify the core issue they are experiencing. Then, we need to check if there are any known solutions or workarounds in our knowledge base. If a solution exists, we should clearly explain it to the customer. If not, we might need to escalate the issue to a specialist. Following these steps will help us provide a comprehensive and helpful response. Now, given the customer's message: 'My order hasn't arrived, and the tracking number shows no updates for a week,' what should be the next step in resolving this?" What type of prompting is this?

A. Zero-shot
B. Chain-of-thought
C. Role-based
D. Few-shot

正解：B

解説：
The prompt explicitly instructs the Large Language Model (LLM) to perform a step-by-step reasoning process before arriving at the final answer. The instructions lay out a sequential series of intermediate steps: "first identify," "then check," "if a solution exists, explain," "if not, escalate." This technique is known as Chain-of-Thought (CoT) Prompting. CoT is a powerful prompt engineering technique where the user or developer explicitly includes intermediate reasoning steps in the prompt. This guides the model to break down a complex, multi-step problem into smaller, manageable, logical steps, significantly improving its reasoning ability and the accuracy of its final output for complex queries like customer service troubleshooting or multi-step analysis.
Zero-shot (A) would be the raw question without any structure.
Few-shot (B) would involve providing examples of successfully solved problems.
Role-based (C) would involve assigning a persona (e.g., "Act as a customer service expert") but would not explicitly mandate the sequential process.
The inclusion of the explicit steps ("first identify," "then check," etc.) is the defining characteristic of Chain-of-Thought prompting.
(Reference: Google's courses on Prompt Engineering classify Chain-of-Thought prompting as the technique that improves reasoning by explicitly giving the model a series of sequential, intermediate steps to follow to arrive at a better answer for complex tasks.)

質問 # 14
A company wants to use generative AI to create a chatbot that can answer customer questions about their products and services. They need to ensure that the chatbot only uses information from the company's official documentation. What should the company do?

A. Use grounding.
B. Use prompt chaining.
C. Use role prompting.
D. Adjust the temperature parameter.

正解：A

解説：
The core requirement is to guarantee that the chatbot only uses information from the company's official documentation and does not rely on its general knowledge base. This is crucial for ensuring factual accuracy, relevance to the company's specific products, and preventing the generation of fabricated or incorrect information (hallucinations).
The specific technique designed to address this challenge is Grounding. Grounding is the process of connecting the Large Language Model's (LLM's) responses to a trusted, verifiable source of information, such as an organization's internal documents, databases, or live data feeds. When an LLM is grounded, it is forced to base its answers only on the provided context, effectively preventing it from drawing on its broad, generalized training data. Grounding is often implemented using a method called Retrieval-Augmented Generation (RAG), particularly with tools like Google Cloud's Vertex AI Search, which indexes the official documentation and feeds the relevant snippets to the model.
Options A, B, and C address different aspects of model output: Role prompting sets the model's persona, adjusting temperature controls creativity, and prompt chaining manages conversation history, but none of these techniques restrict the model's source of truth to the official documentation. Therefore, Grounding is the correct and most effective technique for this requirement.

質問 # 15
A company's development team is eager to start building generative AI solutions with Google Cloud, but has limited experience in AI development. They need to launch their gen AI solution quickly. What Google Cloud benefit would help the company achieve their goal?

A. Google Cloud's comprehensive training materials and tutorials to help developers.
B. Google Cloud's pre-trained models and low-and no-code AI tools and services.
C. Google Cloud's collaborative AI community and support forums connect developers with AI experts.
D. Google Cloud's focus on continuous improvement provides access to the latest AI tools, features, and best practices.

正解：B

解説：
For a team with limited AI experience needing to launch quickly, leveraging pre-trained models (foundation models) and low-code/no-code tools significantly reduces the development burden and accelerates time to market. This allows them to build and deploy generative AI solutions without requiring deep expertise from scratch. While other options are helpful, this directly addresses the need for quick launch with limited experience.

質問 # 16
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