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Raj Marathi

成为会员时间：2026

钻石联赛

22807 积分
大型语言模型简介 Earned Oct 2, 2026 EDT
生成式 AI 简介 Earned Oct 2, 2026 EDT
利用 Vertex AI 实现机器学习运维 (MLOps)：模型评估 Earned Oct 2, 2026 EDT
Build Data Lakes and Data Warehouses on Google Cloud Earned Sep 5, 2026 EDT
Build Streaming Data Pipelines on Google Cloud Earned Sep 5, 2026 EDT
Serverless Data Processing with Dataflow: Operations Earned Sep 5, 2026 EDT
部署多智能体架构 Earned Aug 30, 2026 EDT
使用智能体开发套件 (ADK) 构建 AI 智能体 Earned Aug 30, 2026 EDT
部署您的第一个智能体 Earned Aug 30, 2026 EDT
在生产环境中构建和部署智能体 Earned Aug 30, 2026 EDT
使用 Gemini Enterprise 编排多智能体工作流 Earned Aug 30, 2026 EDT
以人为本的 AI Earned Aug 30, 2026 EDT
生成式 AI 智能体：助力组织转型 Earned Aug 30, 2026 EDT
Build Agent Skills with Google Earned Aug 30, 2026 EDT
使用 ADK 和 MCP 构建协作式多智能体系统 Earned Aug 30, 2026 EDT
Model Armor：保障 AI 部署安全 Earned Aug 30, 2026 EDT
Google Cloud Agent Governance and Security Earned Aug 30, 2026 EDT
保护企业 AI 智能体 Earned Aug 30, 2026 EDT
管理智能体记忆和状态 Earned Aug 30, 2026 EDT
优化智能体行为 Earned Aug 30, 2026 EDT
使用智能体开发套件 (ADK) 打造您的首个智能体 Earned Aug 30, 2026 EDT
利用工具为智能体注入强大功能 Earned Aug 30, 2026 EDT
打造您的首个 Gemini Enterprise 应用 Earned Aug 30, 2026 EDT
企业智能体及其应用场景 Earned Aug 30, 2026 EDT
智能体基础知识 Earned Aug 30, 2026 EDT
Serverless Data Processing with Dataflow: Foundations Earned Aug 23, 2026 EDT
Build Batch Data Pipelines on Google Cloud Earned Aug 23, 2026 EDT
在 BigQuery 中使用 Gemini 模型 Earned Aug 23, 2026 EDT
Google Cloud 数据工程简介 Earned Aug 23, 2026 EDT
Serverless Data Processing with Dataflow: Develop Pipelines Earned Aug 23, 2026 EDT
使用 BigQuery 构建数据仓库 Earned Jul 29, 2026 EDT
Build a Certification Study Guide: PDE Exam Prep Earned Jul 29, 2026 EDT
使用 Knowledge Catalog 构建数据网格 Earned Jul 29, 2026 EDT
云端数据管理和存储 Earned Mar 13, 2026 EDT
融会贯通：为云数据分析师岗位做好准备 Earned Mar 13, 2026 EDT
讲故事的力量：如何在云端可视化数据 Earned Mar 13, 2026 EDT
云端数据转换 Earned Mar 13, 2026 EDT
在 Agent Platform 中设计提示 Earned Mar 12, 2026 EDT
用 AppSheet 来构建应用 Earned Mar 12, 2026 EDT
在 Google 表格中使用函数、公式和图表 Earned Mar 12, 2026 EDT
在关联工作表中分析 BigQuery 数据 Earned Mar 12, 2026 EDT
实现云协作和生产力工作流 Earned Mar 11, 2026 EDT
Data Studio Essentials Earned Mar 11, 2026 EDT
Arcade March 2026 Sprint 4 Earned Mar 11, 2026 EDT
BigQuery for Data Analysts Earned Mar 10, 2026 EDT
Arcade March 2026 Sprint 3 Earned Mar 10, 2026 EDT
Arcade March 2026 Sprint 2 Earned Mar 10, 2026 EDT
Arcade March 2026 Sprint 1 Earned Mar 9, 2026 EDT
Arcade Base Camp March 2026 Earned Mar 9, 2026 EDT
Arcade Voyage: AI and Cloud Deployment Earned Mar 9, 2026 EDT
Arcade Adventure: Security, Data, and Cloud Operations Earned Mar 5, 2026 EST
Arcade Trail: Automation and Analytics Earned Mar 4, 2026 EST
Holi-istic Infrastructures Earned Mar 3, 2026 EST
Skills At The Pitch Earned Feb 27, 2026 EST
Arcade Certification Zone February 2026 Earned Feb 26, 2026 EST
From Foundations To Wonders Earned Feb 25, 2026 EST
Arcade Trail: Application Development Earned Feb 23, 2026 EST
Arcade Adventure: Analytics and Automation Earned Feb 23, 2026 EST
适用于生成式 AI 的机器学习运维 (MLOps) Earned Feb 19, 2026 EST
Developing Data Models with LookML Earned Feb 19, 2026 EST
Analyzing and Visualizing Data in Looker Earned Feb 18, 2026 EST
在 Looker 中应用高级 LookML 概念 Earned Feb 18, 2026 EST
在 Looker 中管理数据模型 Earned Feb 18, 2026 EST
使用 Gemini in BigQuery 提高效率 Earned Feb 17, 2026 EST
为 Looker 信息中心和报告准备数据 Earned Feb 16, 2026 EST
Google Cloud 数据分析基础知识 Earned Feb 16, 2026 EST
Introduction to Looker Earned Feb 15, 2026 EST
Arcade Base Camp February 2026 Earned Feb 15, 2026 EST
Google Cloud 数据分析功能简介 Earned Feb 7, 2026 EST
从 BigQuery 数据中挖掘数据洞见 Earned Feb 7, 2026 EST
通过 BigQuery ML 创建机器学习模型 Earned Feb 5, 2026 EST

这是一节入门级微学习课程，探讨什么是大型语言模型 (LLM)、适合的应用场景以及如何使用提示调整来提升 LLM 性能，还介绍了可以帮助您开发自己的 Gen AI 应用的各种 Google 工具。

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这是一节入门级微课程，旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。

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本课程能让机器学习从业者掌握评估生成式和预测式 AI 模型的基本工具、方法和最佳实践。要确保机器学习系统在实际运用中提供可靠、准确、高效的结果，做好模型评估至关重要。 学员将深入了解各项评估指标、方法及如何在不同模型类型和任务中适当应用这些指标和方法。课程将着重介绍生成式 AI 模型带来的独特挑战，并提供有效解决这些挑战的策略。通过利用 Google Cloud 的 Vertex AI Platform，学员可学习如何在模型选择、优化和持续监控工作中实施卓有成效的评估流程。

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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.

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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.

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完成部署多智能体架构这一高级技能徽章课程，展示您在以下方面的技能： 使用 ADK 构建多智能体系统；通过 Agent-to-Agent (A2A) protocol 连接智能体， 通过 Model Context Protocol (MCP) 集成外部工具，并将完整的多智能体解决方案部署到 Agent Engine。

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完成本课程，赢取使用智能体开发套件 (ADK) 构建 AI 智能体中级技能徽章，展示您在以下方面的技能： 提出实际的语言模型研究问题；构建一个简单的词元化器；制作用于训练 Transformer 语言模型的数据集；运行小语言模型的训练循环。

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将智能体从 localhost 部署到生产环境。本课程将介绍如何将 ADK 智能体部署到 Vertex AI Agent Engine 和 Cloud Run，以及如何通过记忆库实现可选的跨会话记忆功能。

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探索如何使用 Google 的智能体开发套件 (ADK) 和 Google Cloud 强大的基础设施来构建和部署多智能体系统。您将学习如何设计分层智能体树和确定性工作流智能体，同时确定理想的托管环境（从无服务器 Cloud Run 到高性能 GKE），以确保您的 AI 智能体安全、可伸缩且可用于生产用途。

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完成入门级使用 Gemini Enterprise 编排多智能体工作流技能徽章课程，展示您在以下方面的技能：使用由 Gemini Enterprise 提供支持的智能体助理整合来自第一方和第三方来源的数据、制作多媒体营销材料，以及跨系统自动执行业务操作。 技能徽章通过动手实验和挑战赛形式的评估，检验您对特定产品的实际知识掌握情况。完成课程即可获得徽章，也可直接参加实验室挑战赛，快速获得徽章。徽章可证明您掌握技能的熟练程度，提升您的专业形象，最终助您获得更多职业机会。欢迎访问您的个人资料，跟踪您已获得的徽章。

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探索从任务驱动型工作流到以人为本的 AI 编排的重大转变。您将学习如何从战略层面区分增强和自动化，以及如何平衡机器效率与人类直觉。

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“生成式 AI 智能体：助力组织转型”是“Gen AI Leader”学习路线中的第五门课程，也是最后一门课程。本课程探讨了组织如何使用量身定制的生成式 AI 智能体，帮助应对特定的业务挑战。您将亲自动手构建一个基本的生成式 AI 智能体，并探索这些智能体的组成部分，例如模型、推理循环以及各种工具。

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Close the gap between general AI capabilities and your domain knowledge through Agent Skills. This course walks you through the full lifecycle of custom skill building, from local scaffolding and testing to enterprise scaling and sharing using Google's agent ecosystem, including Google Antigravity, ADK, and Skill Registry. Additionally, you use Google Agents CLI toolkit to attach skills to standalone 24/7 cloud agents, equipping your team with reliable, autonomous digital coworkers.

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学习如何使用 Google 的智能体开发套件 (ADK) 和 Model Context Protocol (MCP) 设计、协调和部署多智能体系统。以旅行礼宾为例，您将学习如何编排由专用智能体组成的网络，并为其配置自定义护栏。学完本课程后，您将了解构建稳健、可伸缩的 AI 工作流所需的设计模式和部署策略。

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本课程回顾了 Model Armor 的基本安全功能，并让您能够使用该服务。您将了解与 LLM 相关的安全风险，以及 Model Armor 如何保护您的 AI 应用。

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As enterprise teams transition to autonomous AI agents, security evolves toward proactive, identity-centric protection. In this course, you will implement a zero-trust security architecture for Cymbal Banking's underwriting platform using the Gemini Enterprise Agent Platform. By deploying a regional Egress Agent Gateway, configuring Model Armor and Semantic Governance (SGP) safety templates, assigning cryptographic SPIFFE identities to agent runtimes, and declaring a VPC-SC perimeter in Terraform, you will protect database boundaries and manage tools with production-grade governance.

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了解如何在 Google Cloud 上保护和治理 AI 智能体。本课程提供了一个实用的路线图，可帮助您保护智能体记忆，通过 IAM 和 VPC Service Controls 实施严格的访问权限控制，并部署实时对话防护措施，从而确保企业部署安全合规。

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您已经构建了能够回答查询的基本 LLM 智能体，现在让我们为其赋予状态。使用会话状态来构建能够维护上下文、记住用户偏好并提供个性化体验的智能体。将智能体从“无状态的回复者”转变为“智能助理”。 探索 Gemini Enterprise Agent Ready (GEAR) 计划中的其他内容。 加入社区论坛，提出问题并参与讨论

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您已经创建了自己的第一个智能体，现在是时候更进一步了。在本课程中，您将通过学习应用高级指令、模型选择、规划功能以及结构化输出模式来精进技能，将基础 AI 智能体升级为一个精确、专业的智能助理。探索 Gemini Enterprise Agent Ready (GEAR) 计划中的其他内容。 加入社区论坛，提出问题并参与讨论。

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使用 Google 的智能体开发套件 (ADK) 构建、配置和运行您的第一个 AI 智能体，将您对智能体的理解转化为实际应用。 在本实操课程中，您将设置一个完整的 ADK 开发环境，使用 Python 代码和 YAML 配置两种方式创建智能体，并通过多个界面运行智能体。您还将学习定义智能体行为的核心参数，将您在课程 1 中学到的知识应用到实际代码中。 探索 Gemini Enterprise Agent Ready (GEAR) 计划中的其他内容。

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您已经构建了具有高级配置的智能体，现在可赋予它们实际应用能力。为智能体配备能够搜索网络、执行代码、查询数据库和执行自定义操作的工具。让智能体从“智能回答者”转变为能够自主采取行动的得力助手。 探索 Gemini Enterprise Agent Ready (GEAR) 计划中的其他内容。 加入社区论坛，提出问题并参与讨论。

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打造您的首个 Gemini Enterprise 应用，赢得技能徽章！将各种数据源连接到您的应用中，构建强大、统一的搜索和分析引擎。掌握进阶能力，如：深度研究型智能体、多智能体协同构思，以及用于进行聚焦式分析的 NotebookLM。

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了解 AI 智能体如何为业务带来实际影响。您将把智能体类型关联到您的关键绩效指标 (KPI)，并探索能够解决实际瓶颈的用例。然后，您将了解 Gemini Enterprise 如何帮助您构建和编排合适的智能体，其范围涵盖从无代码到高代码的各种解决方案。

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本课程将介绍 AI 智能体的基础知识，并探讨智能体在现实世界中的价值所在。它为想要从自主、目标导向行为的角度理解 AI 系统的开发者、架构师和技术决策者奠定了基础。

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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.

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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

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本课程展示了如何在 BigQuery 中使用 AI/机器学习模型处理生成式 AI 任务。通过一个涉及客户关系管理的实际应用场景，您将学习到使用 Gemini 模型解决业务问题的工作流程。为了便于理解，本课程还将通过使用 SQL 查询和 Python 笔记本的编码解决方案提供分步指导。

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在本课程中，您将了解 Google Cloud 数据工程、数据工程师的角色和职责，以及相关的 Google Cloud 产品和服务。您还将了解如何应对数据工程挑战。

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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

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完成中级技能徽章课程使用 BigQuery 构建数据仓库，展示以下技能： 联接数据以创建新表、排查联接故障、使用并集附加数据、创建日期分区表， 以及在 BigQuery 中使用 JSON、数组和结构体。

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Learn how to use Gemini Notebook to create a personalized study guide for the Professional Data Engineer certification exam. You'll review Gemini Notebook features, create a notebook in Gemini Notebook, and learn how to use a study guide to practice for a certification exam.

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完成入门技能徽章课程使用 Knowledge Catalog 构建数据网格，展示以下方面的技能：使用 Knowledge Catalog 构建数据网格， 以在 Google Cloud 上实现数据安全、治理和发现。您将在 Knowledge Catalog 中练习和测试自己在标记资产、分配 IAM 角色和评估数据质量方面的技能。

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本课程是 Google Cloud 数据分析认证计划的第二门课程（共五门）。在本课程中，您将探索数据的结构形式和组织方式。您将获得数据湖仓一体架构和云组件（如 BigQuery、Google Cloud Storage 和 DataProc）的实操经验，以便高效地存储、分析和处理大型数据集。

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本课程是 Google Cloud 数据分析认证计划的第五门课程（共五门）。在本课程中，你将综合运用前 4 门课程所学的基础知识和技能，实操完成一个结业项目，全面探索整个数据生命周期。您将练习使用云端工具来有效地获取、存储、处理、分析、直观呈现数据并传达数据分析洞见。课程结束时，您将完成一个项目，证明您在以下方面的熟练程度：高效地设计数据结构以整理来自多个来源的数据、向不同利益相关方展示解决方案，以及使用云端软件直观呈现数据分析洞见。您还将更新个人简历并练习面试技巧，为求职申请与面试环节做好准备。

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本课程是 Google Cloud 数据分析认证计划的第四门课程（共五门课程）。在本课程中，您将重点学习在云端可视化数据的相关技能，其中数据可视化可分为五个关键阶段：讲故事、规划、探索数据、构建可视化图表以及与他人共享数据。您还将获得实操经验，尝试使用 UI（界面）/UX（用户体验）技能来制作线框图，从而设计出有影响力的云原生可视化图表，并使用云原生数据可视化工具来探索数据集、创建报告和构建信息中心，从而推动决策并促进协作。

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本课程是 Google Cloud 数据分析认证计划的第三门课程（共五门）。在本课程中，您将首先了解从收集数据到获取数据分析洞见的整个数据历程。然后，您将学习如何使用 SQL 将原始数据转换为可用格式。接下来，您将学习如何使用数据流水线转换大量数据。最后，您会获得相关经验，熟悉如何将数据转换策略应用于真实数据集以满足业务需求。

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完成 在 Agent Platform 中设计提示入门技能徽章课程，展示以下方面的技能： Agent Platform 中的提示工程、图片分析和多模态生成式技术。探索如何编写有效的提示，指导生成式 AI 输出， 以及将 Gemini 模型应用于真实的营销场景。

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完成用 AppSheet 来构建应用课程，赢取技能徽章。 在此课程中，您将学习如何使用 AppSheet 构建、配置和发布应用。

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完成中级技能徽章课程“在 Google 表格中使用函数、公式和图表”， 展现您在以下方面的技能：使用函数分析数据；使用图表直观呈现数据；以及搜索和验证数据、 设置数据格式、显示数据。

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完成在关联工作表中分析 BigQuery 数据这一技能徽章课程，展示您能够使用关联工作表，在 Google 表格的电子表格中访问、分析、可视化并共享数十亿行 BigQuery 数据。

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完成 实现云协作和生产力工作流课程，赢取入门技能徽章。在此课程中，您将了解 Google 的协作平台 并学习如何使用 Gmail、Google 日历、Meet、Google 云端硬盘、Google 表格和 AppSheet。

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This course provides an introduction to Data Studio’s powerful features for data visualization and reporting. Learn to transform raw data into insightful reports by mastering various visualization options, connecting to diverse data sources, and implementing interactive controls such as filters. Explore data blending techniques to combine information from multiple sources and unlock deeper insights. Through hands-on exercises you'll gain the skills to create compelling, dynamic reports that effectively communicate data-driven stories.

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This month's Arcade Sprint 4 is live! It's designed for quick learning. Since the labs are open all month, there's no rush; just jump in when you have the time. You'll earn 1 Arcade Point and a new skill with each step. It's that simple: grab your point and keep the momentum going!

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This course is designed for data analysts who want to learn about using BigQuery for their data analysis needs. Through a combination of videos, labs, and demos, we cover various topics that discuss how to ingest, transform, and query your data in BigQuery to derive insights that can help in business decision making.

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This month's Arcade Sprint 3 is live! It's designed for quick learning. Since the labs are open all month, there's no rush; just jump in when you have the time. You'll earn 1 Arcade Point and a new skill with each step. It's that simple: grab your point and keep the momentum going!

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This month's Arcade Sprint 2 is live! It's designed for quick learning. Since the labs are open all month, there's no rush; just jump in when you have the time. You'll earn 1 Arcade Point and a new skill with each step. It's that simple: grab your point and keep the momentum going!

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This month's Arcade Sprint 1 is live! It's designed for quick learning. Since the labs are open all month, there's no rush; just jump in when you have the time. You'll earn 1 Arcade Point and a new skill with each step. It's that simple: grab your point and keep the momentum going!

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Welcome to Base Camp, where you’ll develop key Google Cloud skills (available in Spanish and Portuguese too!) and earn an exclusive credential that will open doors to the cloud for you. No prior experience is required!

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Over 85% of enterprises are accelerating AI and cloud adoption to stay ahead. In this voyage, you’ll prepare and protect data using Dataprep and the Cloud Data Loss Prevention API, scan for vulnerabilities, and validate workloads. You’ll also build and deploy modern applications using Cloud Build, GKE pipelines, App Engine Flexible, and Artifact Registry — while creating AI-powered solutions with Vertex AI, Gemini, and Speech-to-Text. Together, these labs help you secure data, deploy confidently, and build intelligent cloud applications at scale.

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Over 80% of security breaches are linked to misconfigurations and weak access controls — which is why mastering cloud security and automation matters. In this adventure, you’ll detect and investigate threats with Security Command Center, create IAM custom roles, and secure digital transactions using Confidential Space. You’ll also strengthen your data and infrastructure skills by working with Datastore, Pub/Sub, Cloud Storage APIs, data joins and warehousing techniques, load balancers, Terraform, and CI/CD pipelines with Jenkins on Kubernetes Engine. Together, these labs help you build secure, reliable, and scalable cloud environments with confidence.

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Modern cloud teams rely on automation and strong platform knowledge to move faster and smarter. In this trail, you’ll deploy Kubernetes Load Balancers and Cloud SQL using Terraform, strengthen observability with Cloud Logging, and customize analytics experiences in Looker using Liquid. You’ll also put your knowledge to the test through role-based Arcade Quizzes — from Data Practitioner and Database Engineer to Workspace Administrator — sharpening both your technical confidence and real-world readiness.

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Holi is all about bringing different colors together — and that’s exactly what this challenge does with technology. You’ll package applications with Docker, manage deployments using Kubernetes Engine, secure access with IAM and VPN, and deploy apps on App Engine. You’ll also store data in Cloud Storage and explore how AI APIs can analyze, classify, and translate text. It’s a hands-on way to see how different cloud pieces come together to build something reliable, secure, and smart.

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Pup and Kit’s World Cup journey highlights how the right skills quietly power great experiences. As they plan, adapt, and stay in sync using data, automation, and cloud-based tools, the story nudges you to step into their shoes and build those capabilities yourself. Each lab mirrors a moment from their adventure, encouraging you to explore, experiment, and strengthen skills that help you stay prepared, move faster, and make smarter choices-whatever the situation demands.

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Google Cloud Certifications provide a tangible way for you to demonstrate your skills to potential or current employers. These certifications incorporate performance-based questions, testing your hands-on expertise through practical tasks. Begin your journey towards becoming a Google Certified Professional with the help of the Arcade Cert Zone.

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As Pup and Kit move through the city between matches—booking plans on the fly, exploring structures built to last, and navigating busy streets—the story reflects what it takes to keep systems running smoothly behind the scenes. From securing storage and shaping networks to improving performance, reliability, and cost efficiency, these labs echo the quiet work that makes flexibility possible. Along the way, hands-on challenges and quizzes sharpen decision-making, so when it’s time for the next big play, everything is ready to perform.

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Developers using managed cloud services can reduce infrastructure management effort by up to 60%, allowing more time to focus on building features that matter. In this level, you’ll gain hands-on experience with Cloud SQL, containerized workloads, and cloud-native application development through the Arcade ACE App Dev series, helping you design, deploy, and manage scalable applications more efficiently.

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Over 70% of cloud professionals say hands-on practice is the most effective way to build real-world skills. In this adventure, you’ll develop hands-on skills across data analytics, automation, security, networking, and application deployment using Google Cloud and Workspace tools. You’ll create dashboards and alerts in Looker, analyze security findings, work with AI-powered services, deploy CI/CD pipelines, and manage network access and traffic insights—strengthening your ability to design, monitor, and operate reliable, scalable cloud solutions.

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本课程致力于为您提供所需的知识和工具，让您能够了解 MLOps 团队在部署和管理生成式 AI 模型以及探索 Vertex AI 如何帮助 AI 团队简化 MLOps 流程时面临的独特挑战，并帮助您在生成式 AI 项目中取得成功。

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This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.

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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.

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在本课程中，您将获得在 Looker 中应用高级 LookML 概念 的实践经验。您将学习如何使用 Liquid 自定义和创建动态 维度和测量、创建动态 SQL 派生表和自定义原生 派生表，并运用扩展功能来模块化你的 LookML 代码

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完成中级技能徽章课程在 Looker 中管理数据 模型，展示以下方面的技能：维护 LookML 项目的健康状况；利用 SQL Runner 进行数据验证；采用 LookML 最佳实践；优化查询和 报告；以及实现永久性派生表和缓存政策。

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此课程将探索如何使用 AI 功能套件 Gemini in BigQuery 为“数据到 AI”工作流提供助力。其中涉及到的功能包括数据探索和准备、代码生成和问题排查，以及工作流发现和可视化。此课程包含概念解释、真实使用场景以及实操实验等内容，可帮助数据从业者提升效率并加快流水线开发速度。

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完成为 Looker 信息中心和报告准备数据入门级技能徽章课程， 展现您在以下方面的技能：对数据进行过滤、排序和透视；将来自不同 Looker 探索的结果合并； 以及使用函数和运算符构建 Looker 信息中心和报告以用于数据分析和可视化。

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了解如何在 Google Cloud 上将原始数字转化为清晰、以数据为依据的决策。通过实操实验和实际应用场景，本新手级课程将引导您在 BigQuery 中查询数据、在 Looker 中探索趋势，以及在数据洞察中制作可共享的交互式报告。

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In this introductory course, you'll learn how Looker can help you explore, analyze, and visualize your data to drive better decisions. Through a combination of video lectures and demos, you'll discover how to connect to various data sources, build interactive dashboards, and perform effective data analysis. Whether you're a data analyst, BI analyst, data scientist or business user, this course will equip you with the foundational knowledge to start using Looker effectively, regardless of your background.

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Welcome to Base Camp, where you’ll develop key Google Cloud skills (available in Spanish and Portuguese too!) and earn an exclusive credential that will open doors to the cloud for you. No prior experience is required!

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本课程是 Google Cloud 数据分析认证的第一门课程（共五门）。在本课程中，您将认识云数据分析领域，并了解云数据分析师在数据获取、存储、处理和可视化方面的角色和职责。您将探索 BigQuery 和 Cloud Storage 等基于 Google Cloud 的工具的架构，以及如何使用这些工具有效地设计数据结构，以及展示和报告数据。

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完成入门级技能徽章课程“从 BigQuery 数据中挖掘数据洞见”，展示您在以下方面的技能： 编写 SQL 查询、查询公共表、将示例数据加载到 BigQuery 中、 在 BigQuery 中使用查询验证器排查常见的语法错误，以及通过连接到 BigQuery 数据在 Data Studio 中 创建报告。

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完成中级技能徽章课程通过 BigQuery ML 创建机器学习模型，展示您在以下方面的技能： 使用 BigQuery ML 创建和评估机器学习模型，以执行数据预测。

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