Google Generative-AI-Leader考試資訊 & Generative-AI-Leader認證

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Google Generative-AI-Leader 考試大綱：

主題	簡介
主題 1	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.
主題 2	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.
主題 3	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.
主題 4	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.

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最新的 Google Cloud Certified Generative-AI-Leader 免費考試真題 (Q95-Q100):

問題 #95
A software developer needs a highly efficient, open-source large language model that can be fine-tuned on a local machine for rapid prototyping of a chatbot application. They require a model that offers strong performance in natural language understanding and generation, while being lightweight enough to run on limited hardware. Which Google-developed family of models should they use?

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

答案：D

解題說明：
Gemma is Google's family of lightweight, state-of-the-art open models, built from the same research and technology used to create the Gemini3 models. They are designed for developers to build innovative AI applications on their local machines or in the cloud, offering a balance of performance and efficiency suitable for limited hardware and rapid prototyping. Veo is for video generation, Gemini is typically larger and more general-purpose, and Imagen is for image generation.
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問題 #96
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 scalable and cost-effective object storage for data used in machine learning workflows.
B. It provides a unified platform of tools for building, deploying, and managing machine learning.
C. It provides a mechanism for efficient analysis and exploration of large datasets used in machine learning.
D. It provides gen AI coding assistance with enterprise security and privacy protection.

答案：B

解題說明：
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.
Option A describes BigQuery.
Option B describes Gemini Code Assist.
Option C describes Cloud Storage.
Vertex AI is the overarching platform that integrates all these tools to deliver a streamlined MLOps workflow.
(Reference: Google Cloud documentation states that Vertex AI is the unified AI development platform that brings together Google Cloud services for building, deploying, and managing machine learning models and generative AI solutions.)

問題 #97
A regional artisan bakery plans to launch a chatbot that accepts custom cake delivery orders. The assistant must guide a structured dialogue so it gathers every required detail before submitting the order, including cake size, flavor choices, and the recipient's delivery address. If a customer says, "I need a medium chocolate cake", the assistant must detect that the address is still missing and ask for it. Which Google Cloud service is designed to run goal directed conversations that identify user intents and extract required entities to complete the task?

A. Gemini API
B. Dialogflow API
C. Natural Language API
D. Vertex AI Search

答案：B

解題說明：
It is designed for goal directed conversations that detect user intents and extract required entities in order to complete a task.
Dialogflow provides intents, entities, and slot filling so it can require all necessary parameters before fulfillment. In the bakery scenario it would recognize the order intent, capture cake size and flavor, realize that the delivery address is missing, and then prompt the user for that address.
Once all required details are gathered it can hand off to fulfillment to place the order.

問題 #98
A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone. What type of machine learning should they use?

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

答案：C

解題說明：
Since the team has not yet assigned any labels or categories to the sensor readings and wants to identify "anomalies, malfunctions, or natural groupings" based on the data alone, this is a classic unsupervised learning problem. Unsupervised learning techniques like clustering or anomaly detection are used to find hidden patterns or structures in unlabeled data.
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問題 #99
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 focus on continuous improvement provides access to the latest AI tools, features, and best practices.
D. Google Cloud's collaborative AI community and support forums connect developers with AI experts.

答案：B

解題說明：
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.
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問題 #100
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