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Tomasz Matyja

Member since 2020

AI 代理基礎知識 Earned Aug 10, 2026 EDT
建立您的第一個 Gemini Enterprise 應用程式 Earned Aug 10, 2026 EDT
部署多代理架構 Earned Jul 17, 2026 EDT
以串流方式將分析資料傳入 BigQuery Earned Jul 17, 2026 EDT
運用 Google Cloud 人工智慧推動創新 Earned Jul 10, 2026 EDT
Google Cloud 的可靠性與安全性 Earned Jul 10, 2026 EDT
為 Google Cloud 設定服務帳戶和 IAM 角色 Earned Jul 10, 2026 EDT
在 Google Cloud 實作 CI/CD 管道 Earned Jul 10, 2026 EDT
AI 代理簡介 Earned May 8, 2026 EDT
Conversational AI Voice and Chat Integrations Earned Aug 11, 2025 EDT
Extend Conversational Agents Functionality with Webhooks and Tools Earned Jul 31, 2025 EDT
Build basic Conversational Agents with Playbooks and Flows Earned Jul 30, 2025 EDT
Extend CX Agents with Agent Search data stores Earned Jul 30, 2025 EDT
Incorporate Generative Features into Conversational Agent Flows Earned Jul 30, 2025 EDT
[DEPRECATED] Stateful Flows Earned Jul 29, 2025 EDT
Generative Playbooks Earned Jul 25, 2025 EDT
Introduction to Gemini for customer experience Earned Jul 24, 2025 EDT
生成式 AI 代理：實現組織轉型 Earned Jul 22, 2025 EDT
生成式 AI 應用程式：徹底改變工作方式 Earned Jul 18, 2025 EDT
生成式 AI：掌握幕後技術與環境 Earned Jul 4, 2025 EDT
生成式 AI：瞭解基礎概念 Earned Jul 3, 2025 EDT
生成式 AI：不只是聊天機器人 Earned Jul 1, 2025 EDT
開始使用 Looker Earned Dec 19, 2024 EST
監控及管理 Google Cloud 資源 Earned Dec 18, 2024 EST
Visualize Your Data in Looker Earned Dec 17, 2024 EST
在 BigQuery 執行預測資料分析 Earned Dec 16, 2024 EST
建立及管理 AlloyDB 執行個體 Earned Dec 13, 2024 EST
使用 Google Data Cloud 共用資料 Earned Dec 12, 2024 EST
Managing Cloud Infrastructure with Terraform Earned Dec 11, 2024 EST
在 Google Cloud 實作 Cloud 安全防護措施：基礎知識 Earned Nov 18, 2024 EST
Google Cloud 的 AI 和機器學習服務簡介 Earned Nov 13, 2024 EST
Google Cloud 中的資料工程簡介 Earned Nov 4, 2024 EST
Building Resilient Streaming Systems on Google Cloud Platform Earned Jul 3, 2024 EDT
Serverless Data Processing with Dataflow: Foundations Earned Jul 2, 2024 EDT
Build Streaming Data Pipelines on Google Cloud Earned Jul 2, 2024 EDT
Build a Certification Study Guide: PDE Exam Prep Earned Jun 19, 2024 EDT
Google Workspace with Gemini：AI 工作流程的基礎 Earned Feb 2, 2024 EST
Build Batch Data Pipelines on Google Cloud Earned Jan 31, 2024 EST
Applying Machine Learning to your Data with Google Cloud Earned Jan 31, 2024 EST
Creating New BigQuery Datasets and Visualizing Insights Earned Jan 29, 2024 EST
Exploring and Preparing your Data with BigQuery Earned Jan 26, 2024 EST
Achieving Advanced Insights with BigQuery Earned Jan 26, 2024 EST
在 Looker 建構 LookML 物件 Earned Jan 24, 2024 EST
Google Workspace 資料治理 Earned Jan 23, 2024 EST
Build Data Lakes and Data Warehouses on Google Cloud Earned Jan 23, 2024 EST
DEPRECATED Planning for a Google Workspace Deployment Earned Jan 5, 2024 EST
Google Workspace 安全性 Earned Jan 5, 2024 EST
Google Workspace 核心服務 Earned Jan 4, 2024 EST
Google Workspace 的使用者和資源管理功能 Earned Jan 3, 2024 EST
[DEPRECATED] Google Calendar Earned Jan 3, 2024 EST
[DEPRECATED] Gmail Earned Jan 2, 2024 EST
圖像生成簡介 Earned Dec 18, 2023 EST
負責任的 AI 技術：透過 Google Cloud 採用 AI 開發原則 Earned Dec 15, 2023 EST
[DEPRECATED] Generative AI Fundamentals Earned Dec 7, 2023 EST
負責任的 AI 技術簡介 Earned Dec 7, 2023 EST
大型語言模型簡介 Earned Dec 5, 2023 EST
[DEPRECATED] Generative AI for Business Leaders Earned Dec 1, 2023 EST
生成式 AI 簡介 Earned Nov 28, 2023 EST
Analyzing and Visualizing Data in Looker Earned Nov 28, 2023 EST
使用 Knowledge Catalog 建構資料網格 Earned Jul 18, 2023 EDT
使用 Dataplex 整理及管理資料 Earned Jul 18, 2023 EDT
建立及管理 Cloud Spanner 執行個體 Earned Jan 9, 2023 EST
建立及管理 PostgreSQL 適用的 Cloud SQL 執行個體 Earned Jan 5, 2023 EST
[DEPRECATED] Enterprise Database Migration Earned Jan 4, 2023 EST
Google Cloud 基礎知識：核心基礎架構 Earned Dec 30, 2022 EST
使用 Google Cloud 作業套件調度資源 Earned Dec 13, 2022 EST
運用 Google Cloud 翻新基礎架構和應用程式 Earned Dec 13, 2022 EST
運用 Google Cloud 探索資料轉換功能 Earned Dec 12, 2022 EST
透過 Google Cloud 進行數位轉型 Earned Dec 9, 2022 EST
Google Cloud Big Data and Machine Learning Fundamentals - Locales Earned Apr 20, 2022 EDT
Looker 資料模型管理 Earned Apr 15, 2022 EDT
在 Looker 應用進階 LookML 概念 Earned Mar 16, 2022 EDT
Delivery Shadowing Earned Mar 7, 2022 EST
Case Studies Earned Mar 3, 2022 EST
Technology + Beyond the UI Earned Feb 25, 2022 EST
Technology + Within the UI Earned Feb 25, 2022 EST
Table Calculations, Pivots, and Visualizations Earned Feb 22, 2022 EST
Building Reports in Looker Earned Feb 21, 2022 EST
Liquid Templates and Parameters Earned Feb 21, 2022 EST
Extends to Keep LookML DRY Earned Feb 21, 2022 EST
Version Control and Caching Earned Feb 21, 2022 EST
Admin Roles and Folder Access Earned Feb 18, 2022 EST
The Modern Data Platform and LookML Earned Feb 18, 2022 EST
Driving Data Culture and Designing Dashboards Earned Feb 16, 2022 EST
Achieving Business Outcomes with Looker Earned Feb 15, 2022 EST
Looker Explained Earned Feb 15, 2022 EST
Developing Data Models with LookML Earned Feb 11, 2022 EST
使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL Earned Feb 4, 2022 EST
[DEPRECATED] Google Cloud Big Data and Machine Learning Fundamentals Earned Jan 25, 2022 EST
為 Looker 資訊主頁和報表準備資料 Earned Dec 24, 2021 EST
瞭解 Looker 中的 LookML Earned Dec 18, 2021 EST
Databases on AWS for Windows Earned Dec 20, 2020 EST
Using the Cloud SDK Command Line Earned Dec 20, 2020 EST
運用 BigQuery ML 建立機器學習模型 Earned Dec 20, 2020 EST
使用 BigQuery 進行機器學習 Earned Dec 20, 2020 EST
NCAA® March Madness®: Bracketology with Google Cloud Earned Dec 19, 2020 EST
Cloud SQL Earned Dec 19, 2020 EST
Data Catalog Fundamentals Earned Dec 18, 2020 EST
透過 BigQuery 建構資料倉儲 Earned Dec 17, 2020 EST
DEPRECATED BigQuery for Marketing Analysts Earned Dec 15, 2020 EST
Google Cloud 必備知識 Earned Dec 15, 2020 EST
從 BigQuery 資料取得深入分析結果 Earned Dec 14, 2020 EST
DEPRECATED BigQuery Basics for Data Analysts Earned Dec 12, 2020 EST
DEPRECATED BigQuery for Data Warehousing Earned Dec 12, 2020 EST
基本概念：基礎架構 Earned Dec 11, 2020 EST
基本概念：資料、機器學習和 AI Earned Dec 11, 2020 EST
使用 BigQuery ML 為預測模型進行資料工程 Earned Dec 9, 2020 EST
[DEPRECATED] Data Engineering Earned Dec 8, 2020 EST
在 Google Cloud 設定應用程式開發環境 Earned Nov 29, 2020 EST
在 Google Cloud 為機器學習 API 準備資料 Earned Nov 28, 2020 EST
在 Compute Engine 導入 Cloud Load Balancing Earned Nov 23, 2020 EST

本課程介紹 AI 代理的基礎知識，並探討代理的實際應用價值。代理可為開發人員、架構師和技術決策者奠定基礎，協助他們從目標導向的自主行為角度來理解 AI 系統。

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建立您的第一個 Gemini Enterprise 應用程式，獲得技能徽章！將各種資料來源連接到您的應用程式，建立強大的統合式搜尋和分析引擎。掌握 Deep Research 代理、多代理構思和 NotebookLM 等進階功能，進行重點分析。

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完成進階的「部署多代理架構」技能徽章課程，證明您具備以下技能： 使用 ADK 建構多代理系統、將代理連結代理對代理 (A2A) 通訊協定、使用 Model Context Protocol (MCP) 整合外部工具，以及將完整的多代理解決方案部署至 Agent Engine。

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完成「以串流方式將分析資料傳入 BigQuery」課程，即可獲得技能徽章。 在此課程中，您將綜合應用 Pub/Sub、Dataflow 和 BigQuery，並以串流方式傳送 資料進行分析。

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人工智慧 (AI) 和機器學習 (ML) 象徵著資訊技術的重大演進，正迅速改變各行各業。在「運用 Google Cloud 人工智慧推動創新」課程中，我們會探討組織如何運用 AI 和機器學習技術，翻轉業務流程。 本課程是 Cloud Digital Leader 學習路徑的一部分，旨在促進個人職能發展，並打造企業的未來。

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組織將資料和應用程式遷移至雲端後，必須因應快速變化的安全性挑戰。本課程將探討雲端安全性的基礎知識、Google Cloud 融入安全考量設計的基礎架構價值，以及縱深防禦策略。此外，還會著重說明如何透過 AI 輔助作業和法規遵循工具，協助組織滿足嚴格的全球法規要求。 本課程是 Cloud Digital Leader 學習路徑的一部分，旨在促進個人職能發展，並打造企業的未來。

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完成「為 Google Cloud 設定服務帳戶和 IAM 角色」課程，即可獲得入門級技能徽章。這門課程會介紹服務帳戶、自訂角色，以及如何使用 gcloud 設定權限。

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完成在 Google Cloud 實作 CI/CD 管道技能徽章中階課程，即可獲得技能徽章。 您將透過本課程學習如何使用 Artifact Registry、Cloud Build 和 Cloud Deploy，並且操作 Google Cloud 控制台、Google Cloud CLI、Cloud Run 和 GKE。本課程會介紹如何建構持續整合 管道、儲存及保護構件、掃描安全漏洞、驗證 已核准的發布版本是否有效。此外，您還會實際操作，將應用程式 同時部署至 GKE 和 Cloud Run。

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瞭解 AI 代理的概念，探索代理如何藉由自主行動及推論解決複雜問題。您將瞭解代理如何透過模型、工具和調度管理程序等技術架構，助您學習、規劃和實現目標。

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Learn about building conversational AI voice and chat integrations, including how telephony systems can connect with Google to enable phone-based interactions within the Conversational AI ecosystem. Explore key topics such as the differences between chat and voice conversations, the writing process for creating conversation scripts, and the beginning of the interrogative series and closing sequence.

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Connect conversational agents to external systems and APIs to expand what agents can do, designing an end-to-end system that is resilient, fault-tolerant and secure.

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Complete the Build basic Conversational Agents with Playbooks and Flows skill badge to demonstrate your proficiency in building virtual agents using traditional NLU and generative-based features. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete the assessment challenge lab, to receive a skill badge that you can share with your network. When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!

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In this course, you'll learn to develop AI agents that answer questions using websites, documents, or structured data. You will explore AI Applications and understand the advantages of data store agents, including their scalability and security. You'll learn about different data store types and also discover how to connect data stores to agents and add personalization for enhanced responses. Finally, you'll gain insights into common search configurations and troubleshooting techniques.

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Explore the Generative AI features for Conversational Agents and how to incorporate them into stateful Flows. Discover the possibilities with Generators, Generative Fallback, and Data Stores, as well as best practices and security settings for using these features.

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Discover flows in Conversational Agents and learn how to build deterministic chat and voice experiences with language models. Explore key concepts like drivers, intents, and entities, and how to use them to create conversational agents.

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Explore Playbooks and their implementation of the ReAct pattern for building conversational agents. You will learn how to construct a Playbook, set up goals and instructions to build a chatbot in natural language, and learn to test and deploy your solution.

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This course explores the different products and capabilities of Gemini for customer experience, including CX Agent Studio, Agent Assist and CX Insights. Additionally, it covers the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel.

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「生成式 AI 代理：實現組織轉型」是 Gen AI Leader 學習路徑的第五堂也是最後一堂課程。本課程將探討組織如何運用自訂生成式 AI 代理，解決特定的業務難題。您將動手練習建構基本的生成式 AI 代理，同時探索這類代理的各種元件，例如模型、推論迴圈和工具。

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「生成式 AI 應用程式：徹底改變工作方式」是 Generative AI Leader 學習路徑的第四門課程。本課程將介紹 Google 的生成式 AI 應用程式,例如 Gemini for Workspace 和NotebookLM,也會引導您瞭解各種概念,像是建立基準、檢索增強生成、建構有效的提示詞,以及打造自動化工作流程等。

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「生成式 AI：掌握幕後技術與環境」是 Gen AI Leader 學習路徑的第三門課程。生成式 AI 正在改變我們的工作方式，以及與周遭世界的互動模式。身為領導者，您要如何駕馭其強大的功能，創造實際的業務成果？在本課程中，您將認識建構生成式 AI 解決方案時的各個層面、Google Cloud 產品與服務，以及選擇解決方案時應考量的因素。

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生成式 AI：瞭解基礎概念是 Gen AI Leader 學習路徑的第二門課程。在本課程中，您將瞭解 AI、機器學習和生成式 AI 的差異，以及各種類型的資料如何協助生成式 AI 解決業務難題，進而掌握生成式 AI 的基礎概念。您還能深入瞭解 Google Cloud 因應基礎模型限制的策略，以及開發、部署安全且負責任的 AI 技術時面臨的主要挑戰。

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「生成式 AI：不只是聊天機器人」是 Gen AI Leader 學習路徑的第一門課程，沒有先修條件。本課程旨在帶領您超越對聊天機器人的基本認識，探索生成式 AI 能為貴組織帶來的真正潛力。您將瞭解基礎模型和提示工程等概念，這些都是發揮生成式 AI 威力的關鍵。本課程也會引導您思考重要事項，協助您為貴組織制定成功的生成式 AI 策略。

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完成「開始使用 Looker」技能徽章課程，即可獲得技能徽章， 您將從中瞭解如何使用 Looker Studio 和 Looker 分析、呈現及整理資料。

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完成「監控及管理 Google Cloud 資源」技能徽章入門課程，即可證明您具備下列技能：授予及撤銷 IAM 權限； 安裝 Monitoring 和 Logging 代理程式；建立、部署及測試事件導向的 Cloud Run 函式。

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This skill badge course aims to unlock the power of data visualization and business intelligence reporting with Looker, and gain hands-on experience through labs.

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完成在 BigQuery 執行預測資料分析技能徽章中階課程， 即可證明您具備下列技能：可匯入 CSV 和 JSON 檔案，在 BigQuery 建立資料集； 可運用 BigQuery 的強大功能與複雜的 SQL 分析概念，包括使用 BigQuery ML 根據足球賽事資料訓練出預期進球模型，評估世界盃進球的精彩程度。

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完成建立及管理 AlloyDB 執行個體技能徽章入門課程， 即可證明自己具備下列技能：執行主要 AlloyDB 作業 和工作、從 PostgreSQL 遷移至 AlloyDB、管理 AlloyDB 資料庫，以及 使用 AlloyDB 資料欄引擎加快數據分析查詢。

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完成「使用 Google Data Cloud 共用資料」課程，即可獲得技能徽章。 在本課程中，您會透過 Google Cloud 資料共用合作夥伴， 使用專屬資料集進行數據分析， 獲得豐富的實務經驗。客戶訂閱這類資料後，即可在自家平台查詢， 並用自己的資料集補強。此外，也能使用圖表 工具，打造客戶專用的資訊主頁。

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In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Terraform, an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.

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完成 在 Google Cloud 實作 Cloud 安全防護措施：基礎知識 技能徽章中階課程， 即可證明您具備下列技能：運用 Identity and Access Management (IAM) 建立及指派角色、 建立及管理服務帳戶、啟用虛擬私有雲 (VPC) 網路中的私人連線、 運用 Identity-Aware Proxy 限制應用程式存取權、 運用 Cloud Key Management Service (KMS) 管理金鑰和已加密資料，以及建立私人 Kubernetes 叢集。

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本課程介紹 Google Cloud 的 AI 和機器學習 (ML) 功能，著重說明如何開發生成式和預測式 AI 專案。我們也會探討「從資料到 AI」整個生命週期都適用的技術、產品和工具，並透過互動式練習，協助資料科學家、AI 開發人員和機器學習工程師精進專業知識。

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在本課程中，您會學到 Google Cloud 上的資料工程、資料工程師的角色與職責，以及這些內容如何對應至 Google Cloud 提供的服務。您也將瞭解處理資料工程難題的許多方法。

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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.

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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 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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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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客戶能透過 Gemini for Google Workspace 在 Google Workspace 使用生成式 AI 功能。本學習路徑會介紹 Gemini 的主要功能，並說明如何在 Google Workspace 善用這些功能，提高生產力和效率。

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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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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.

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This is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Looker Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources. Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you. After completing this course, enroll in the Achieving Advanced Insights with BigQuery course.

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In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets. This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.

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The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps. We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views. After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.

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完成「在 Looker 建構 LookML 物件」技能徽章入門課程， 即可證明您具備下列技能： 建立新的維度和測量指標、檢視畫面和衍生資料表；根據需求設定測量指標篩選器和類型； 更新維度和測量指標； 建構及調整「探索」；將檢視表彙整至現有「探索」；以及配合業務需求決定要建立哪些 LookML 物件。

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本課程可助您培養在 Google Workspace 環境管理資料的技能。首先，您會瞭解如何在 Gmail 和雲端硬碟，運用資料遺失防護規則防止資料外洩。接著，您將學習使用 Google 保管箱保留、保存及檢索資料。再來，我們會說明如何設定資料地區和匯出方式，以遵守法規要求。最後，您會學習運用標籤區分資料，更安全有條理地管理資料。

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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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Planning for a Google Workspace Deployment is the final course in the Google Workspace Administration series. In this course, you will be introduced to Google's deployment methodology and best practices. You will follow Katelyn and Marcus as they plan for a Google Workspace deployment at Cymbal. They'll focus on the core technical project areas of provisioning, mail flow, data migration, and coexistence, and will consider the best deployment strategy for each area. You will also be introduced to the importance of Change Management in a Google Workspace deployment, ensuring that users make a smooth transition to Google Workspace and gain the benefits of work transformation through communications, support, and training. This course covers theoretical topics, and does not have any hands on exercises. If you haven’t already done so, please cancel your Google Workspace trial now to avoid any unwanted charges.

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本課程說明如何保護 Google Workspace 環境。學員將導入高強度密碼政策和兩步驟驗證，控管使用者存取權，然後使用安全調查工具，主動找出並應對安全風險。接著，學員將管理第三方應用程式存取權和行動裝置，確保安全無虞。最後，學員將強制執行電子郵件安全性和法規遵循措施，保護機構資料。

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本課程旨在讓學員全面瞭解 Google Workspace 核心服務。學員將瞭解如何啟用、停用及設定這些服務，包括 Gmail、日曆、雲端硬碟、Meet、Chat 和文件。接著，他們將學習如何部署及管理 Gemini，為使用者提供強大助力。最後，學員將瞭解 AppSheet 和 Apps Script 的應用實例，學習如何自動處理工作，並擴充 Google Workspace 應用程式的功能。

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本課程介紹 Google Workspace 的使用者和資源管理功能，學員將瞭解如何根據組織需求來設定組織單位、管理各類型的 Google 群組、培養有關管理 Google Workspace 網域設定的專業知識，最後學會如何最佳化及整理 Google Workspace 環境內的資源。

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With Google Calendar, you can quickly schedule meetings and events and create tasks, so you always know what’s next. Google Calendar is designed for teams, so it’s easy to share your schedule with others and create multiple calendars that you and your team can use together. In this course, you’ll learn how to create and manage Google Calendar events. You will learn how to update an existing event, delete and restore events, and search your calendar. You will understand when to apply different event types such as tasks and appointment schedules. You will explore the Google Calendar settings that are available for you to customize Google Calendar to suit your way of working. During the course you will learn how to create additional calendars, share your calendars with others, and access other calendars in your organization.

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Gmail is Google’s cloud based email service that allows you to access your messages from any computer or device with just a web browser. In this course, you’ll learn how to compose, send and reply to messages. You will also explore some of the common actions that can be applied to a Gmail message, and learn how to organize your mail using Gmail labels. You will explore some common Gmail settings and features. For example, you will learn how to manage your own personal contacts and groups, customize your Gmail Inbox to suit your way of working, and create your own email signatures and templates. Google is famous for search. Gmail also includes powerful search and filtering. You will explore Gmail’s advanced search and learn how to filter messages automatically.

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本課程將介紹擴散模型，這是一種機器學習模型，近期在圖像生成領域展現亮眼潛力。概念源自物理學，尤其深受熱力學影響。過去幾年來，在學術界和業界都是炙手可熱的焦點。在 Google Cloud 中，擴散模型是許多先進圖像生成模型和工具的基礎。課程將介紹擴散模型背後的理論，並說明如何在 Vertex AI 上訓練和部署這些模型。

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隨著企業持續擴大使用人工智慧和機器學習，以負責任的方式發展相關技術也日益重要。對許多企業來說，談論負責任的 AI 技術可能不難，如何付諸實行才是真正的挑戰。如要瞭解如何在機構中導入負責任的 AI 技術，本課程絕對能助您一臂之力。 您可以從中瞭解 Google Cloud 目前採取的策略、最佳做法和經驗談，協助貴機構奠定良好基礎，實踐負責任的 AI 技術。

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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI. A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.

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這個入門微學習課程主要介紹「負責任的 AI 技術」和其重要性，以及 Google 如何在自家產品中導入這項技術。本課程也會說明 Google 的 7 個 AI 開發原則。

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這是一堂入門級的微學習課程，旨在探討大型語言模型 (LLM) 的定義和用途，並說明如何調整提示來提高 LLM 成效。此外，也會介紹多項 Google 工具，協助您自行開發生成式 AI 應用程式。

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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.

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這個入門微學習課程主要說明生成式 AI 的定義和使用方式，以及此 AI 與傳統機器學習方法的差異。本課程也會介紹各項 Google 工具，協助您開發自己的生成式 AI 應用程式。

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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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完成「使用 Knowledge Catalog 建構資料網格」技能徽章入門課程，即可證明您具備下列技能：使用 Knowledge Catalog 建構資料網格， 以利在 Google Cloud 維護資料安全性，並協助治理和探索資料。您將練習並測試自己的技能，包括在 Knowledge Catalog 為資產加上標記、指派 IAM 角色，以及評估資料品質。

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完成「使用 Dataplex 整理及管理資料」技能徽章入門課程，即可證明您具備下列技能：建立 Dataplex 資產、建立切面類型，以及將切面套用至 Dataplex 中的項目。 技能徽章課程透過實作實驗室和挑戰評量，檢驗學員對於特定產品的實作知識。 完成課程或直接進行挑戰實驗室，即可取得徽章。徽章可證明您的專業能力、提升專業形象，開創更多職涯發展機會。前往個人資料頁面，追蹤您獲得的徽章。

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完成「建立及管理 Cloud Spanner 執行個體」技能徽章入門課程，即可證明自己具備下列技能： 建立 Cloud Spanner 執行個體和資料庫，並與其互動； 使用各種技術載入 Cloud Spanner 資料庫； 備份 Cloud Spanner 資料庫；定義結構定義及瞭解查詢計畫；以及 部署連線至 Cloud Spanner 執行個體的現代化網頁應用程式。

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完成建立及管理 PostgreSQL 適用的 Cloud SQL 執行個體技能徽章入門課程，證明您具備下列技能：遷移、設定和管理 PostgreSQL 適用的 Cloud SQL 執行個體和資料庫。

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This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to Google Cloud while taking advantage of various services. This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.

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「Google Cloud 基礎知識：核心基礎架構」介紹了在使用 Google Cloud 時會遇到的重要概念和術語。本課程會透過影片和實作實驗室，介紹並比較 Google Cloud 的多種運算和儲存服務，同時提供重要的資源和政策管理工具。

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各種規模的機構都開始運用雲端的強大功能和靈活性，徹底改變營運方式。不過，有效管理及擴充雲端資源可能是相當複雜的工作。本課程將探討在雲端中的現代營運、可靠性和韌性的基礎概念，以及 Google Cloud 如何協助您達成這些目標。本課程是 Cloud Digital Leader 學習路徑的一部分，旨在促進個人職能發展，並打造企業的未來。

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許多傳統企業使用舊版系統和應用程式，無法滿足現今客戶的期望。企業主管經常需要在維護老舊的 IT 系統，以及投資新產品/服務之間做出選擇。本課程將探討這些挑戰，並提供解決方案，說明如何運用雲端技術克服難題。 本課程是 Cloud Digital Leader 學習路徑的一部分，旨在促進個人職能發展，並打造企業的未來。

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雲端技術是強大的資產，與資料搭配後，將成為創新和提升客戶體驗的催化劑。「運用 Google Cloud 探索資料轉換功能」課程將探討機構如何運用雲端，讓資料更易於存取、做為行動依據和更有價值。 這門課程是 Cloud Digital Leader 學習路徑的一部分，旨在協助個人在職務上成長，並打造企業的未來。

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數位轉型是現代組織必經的關鍵旅程，而建立穩固的雲端運算基礎，是跨出有意義創新的第一步。本課程「透過 Google Cloud 進行數位轉型」，將會介紹核心技術和策略框架，協助組織革新營運方式，並探討基本的雲端概念、全球網路基礎架構和共同責任模式，幫助主管在雲端之路上自信前行。本課程是 Cloud Digital Leader 學習路徑的一部分，旨在促進個人職能發展，並打造企業的未來。

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This course, Google Cloud Big Data and Machine Learning Fundamentals - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Google Cloud Big Data and Machine Learning Fundamentals. This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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完成 Looker 資料模型管理技能徽章中階課程，即可證明您具備下列技能：維護 LookML 專案的健全性、運用 SQL Runner 驗證資料、採用 LookML 最佳做法、改良查詢及 報表，並執行永久衍生資料表和快取政策。

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在本課程中，您將透過實際操作，瞭解如何在 Looker 應用進階 LookerML 概念。您將學習如何使用 Liquid 自訂和建立動態維度 和測量指標、建構動態 SQL 衍生資料表和自訂的原生衍生資料表， 並運用擴充參數將 LookML 程式碼模組化。

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In this course, you shadow a series of client meetings led by a Looker Professional Services Consultant.

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By the end of this course, you should feel confident employing technical concepts to fulfill business requirements and be familiar with common complex design patterns.

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In this course you will discover additional tools for your toolbox for working with complex deployments, building robust solutions, and delivering even more value.

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Develop technical skills beyond LookML along with basic administration for optimizing Looker instances

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This course reviews the processes for creating table calculations, pivots and visualizations

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This course is designed for Looker users who want to create their own ad-hoc reports. It assumes experience of everything covered in our Get Started with Looker course (logging in, finding Looks & dashboards, adjusting filters, and sending data)

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In this course you will discover Liquid, the templating language invented by Shopify and explore how it can be used in Looker to create dynamic links, content, formatting, and more.

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Hands on course covering the main uses of extends and the three primary LookML objects extends are used on as well as some advanced usage of extends.

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This course aims to introduce you to the basic concepts of Git: what it is and how it's used in Looker. You will also develop an in-depth knowledge of the caching process on the Looker platform, such as why they are used and why they work

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This course is designed to teach you about roles, permission sets and model sets. These are areas that are used together to manage what users can do and what they can see in Looker.

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This course provides an introduction to databases and summarized the differences in the main database technologies. This course will also introduce you to Looker and how Looker scales as a modern data platform. In the lessons, you will build and maintain standard Looker data models and establish the foundation necessary to learn Looker's more advanced features.

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This course provides an iterative approach to plan, build, launch, and grow a modern, scalable, mature analytics ecosystem and data culture in an organization that consistently achieves established business outcomes. Users will also learn how to design and build a useful, easy-to-use dashboard in Looker. It assumes experience with everything covered in our Getting Started with Looker and Building Reports in Looker courses.

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In this course, we’ll show you how organizations are aligning their BI strategy to most effectively achieve business outcomes with Looker. We'll follow four iterative steps: Plan, Build, Launch, Grow, and provide resources to take into your own services delivery to build Looker with the goal of achieving business outcomes.

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By the end of this course, you should be able to articulate Looker's value propositions and what makes it different from other analytics tools in the market. You should also be able to explain how Looker works, and explain the standard components of successful service delivery.

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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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完成 使用資料庫遷移服務將 MySQL 資料遷移至 Cloud SQL 技能徽章入門課程，證明您具備下列技能： 使用「資料庫遷移服務」中各種可用的工作類型和連線選項， 將 MySQL 資料遷移至 Cloud SQL，以及在執行「資料庫遷移服務」工作時 遷移 MySQL 使用者資料。

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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

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完成「為 Looker 資訊主頁和報表準備資料」技能徽章入門課程， 即可證明您具備下列技能：可篩選、排序和 pivot 資料、合併不同的 Looker 探索結果， 還能使用函式和運算子建構 Looker 資訊主頁和報表，取得資料分析結果和圖表。

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在本課程中，您將實際操作 Looker 的 LookML。您將學習如何編寫 LookML 程式碼，建立新的維度和測量指標、建立衍生資料表並彙整至探索、篩選探索，以及在 LookML 中定義快取政策。

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Learn how to deploy and administer databases running on Microsoft Windows Server in Amazon EC2 and Amazon RDS.

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For everyone using Google Cloud Platform for the first time, getting familar with gcloud, Google Cloud's command line, will help you get up to speed faster. In this quest, you'll learn how to install and configure Cloud SDK, then use gcloud to perform some basic operations like creating VMs, networks, using BigQuery, and using gsutil to perform operations.

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完成「運用 BigQuery ML 建立機器學習模型」技能徽章中階課程，即可證明您具備下列技能： 可使用 BigQuery ML 建立及評估機器學習模型，並根據資料進行預測。

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不想花費大把時間，想在幾分鐘內只靠 SQL，就建立好機器學習模型嗎？透過 BigQuery ML，資料分析師可以運用現有的 SQL 工具和技巧，建立、訓練、評估模型， 並使用模型進行預測，降低機器學習的使用門檻。在 本系列的實驗室，您會測試不同類型的模型，瞭解 優良模型應具備的條件。

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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.

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Cloud SQL is a fully managed database service that stands out from its peers due to high performance, seamless integration, and impressive scalability. In this quest you will receive hands-on practice with the basics of Cloud SQL and quickly progress to advanced features, which you will apply to production frameworks and application environments. From creating instances and querying data with SQL, to building Deployment Manager scripts and connecting Cloud SQL instances with applications run on GKE containers, this quest will give you the knowledge and experience needed so you can start integrating this service right away.

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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.

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完成 透過 BigQuery 建構資料倉儲 技能徽章中階課程，即可證明您具備下列技能： 彙整資料以建立新資料表、排解彙整作業問題、利用聯集附加資料、建立依日期分區的資料表， 以及在 BigQuery 使用 JSON、陣列和結構體。

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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

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在這堂入門課程，您將實際練習使用 Google Cloud 的基礎工具和服務。本課程包含可選擇觀賞的影片， 針對實驗室涵蓋的概念提供更多背景資訊，協助您複習。「Google Cloud 必備知識」 是適合 Google Cloud 學員的第一堂課， 即使您尚未學習或不熟悉雲端知識， 也能從這堂課獲得實務經驗，並應用於第一項 Google Cloud 專案。不管是撰寫 Cloud Shell 指令 和部署第一部虛擬機器，還是在 Kubernetes Engine 或透過負載平衡執行應用程式， 「Google Cloud 必備知識」都是認識平台基本功能的最佳入門資源。

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完成 從 BigQuery 資料取得深入分析結果 技能徽章入門課程，即可證明您具備下列技能： 撰寫 SQL 查詢、查詢公開資料表、將樣本資料載入 BigQuery、使用 BigQuery 的查詢驗證工具 排解常見語法錯誤，以及在 Data Studio 中 透過連結 BigQuery 資料建立報表。

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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.

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Looking to build or optimize your data warehouse? Learn best practices to Extract, Transform, and Load your data into Google Cloud with BigQuery. In this series of interactive labs you will create and optimize your own data warehouse using a variety of large-scale BigQuery public datasets. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.

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如果您是剛起步的雲端開發人員， 想在 Google Cloud Essentials 外獲得更多實作經驗，歡迎參加本課程。您將透過實作實驗室， 深入瞭解 Cloud Storage 和其他重要應用程式服務，例如： Monitoring 和 Cloud Functions。您將習得 在任何 Google Cloud 專案都適用的寶貴技能。

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大數據、機器學習和人工智慧 (AI) 是時下熱門的 電腦相關話題，但這些領域相當專業，就算想要入門 也難以取得教材或資料。幸好，Google Cloud 提供了此領域的多種服務，而且容易使用。 參加這堂入門課程，您就能踏出第一步， 開始學習運用 BigQuery、Cloud Speech API 以及 Video Intelligence 等工具。

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完成使用 BigQuery ML 為預測模型進行資料工程技能徽章中階課程， 即可證明自己具備下列知識與技能：運用 Dataprep by Trifacta 建構連至 BigQuery 的資料轉換 pipeline； 使用 Cloud Storage、Dataflow 和 BigQuery 建構「擷取、轉換及載入」(ETL) 工作負載， 以及使用 BigQuery ML 建構機器學習模型。

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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.

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只要修完「在 Google Cloud 設定應用程式開發環境」課程，就能獲得技能徽章。 在本課程中，您將學會如何使用以下技術的基本功能，建構和連結以儲存空間為中心的雲端基礎架構：Cloud Storage、Identity and Access Management、Cloud Functions 和 Pub/Sub。

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完成 在 Google Cloud 為機器學習 API 準備資料 技能徽章入門課程，即可證明您具備下列技能： 使用 Dataprep by Trifacta 清理資料、在 Dataflow 執行資料管道、在 Managed Service for Apache Spark 建立叢集和執行 Apache Spark 工作，以及呼叫機器學習 API，包含 Cloud Natural Language API、Google Cloud Speech-to-Text API 和 Video Intelligence API。

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完成「在 Compute Engine 導入 Cloud Load Balancing」技能徽章入門課程，即可證明您具備下列技能： 在 Compute Engine 建立及部署虛擬機器， 以及設定網路和應用程式負載平衡器。

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