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在 Google Cloud 控制台中运用您的技能

Yuki Fukuhara

成为会员时间：2021

白银联赛

8910 积分
Security Best Practices in Google Cloud Earned Feb 5, 2022 EST
了解自己的 Google Cloud 成本 Earned Feb 4, 2022 EST
Understanding LookML in Looker Earned Feb 3, 2022 EST
在 Google Cloud 中使用 Kubernetes Earned Jan 31, 2022 EST
在 Google Cloud 上部署 Kubernetes 应用 Earned Jan 31, 2022 EST
DEPRECATED Exploring APIs Earned Jan 28, 2022 EST
Machine Learning APIs Earned Jan 27, 2022 EST
Scientific Data Processing Earned Jan 24, 2022 EST
Data Science on Google Cloud Earned Jan 24, 2022 EST
利用 BigQuery ML 构建预测模型时的数据工程处理 Earned Jan 21, 2022 EST
DEPRECATED BigQuery Basics for Data Analysts Earned Jan 10, 2022 EST
云工程 Earned Jan 9, 2022 EST

This self-paced training course gives participants broad study of security controls and techniques on Google Cloud. Through recorded lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure Google Cloud solution, including Cloud Storage access control technologies, Security Keys, Customer-Supplied Encryption Keys, API access controls, scoping, shielded VMs, encryption, and signed URLs. It also covers securing Kubernetes environments.

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本课程最适合担任技术或财务职务 且负责管理 Google Cloud 费用的人员。您将学习如何设置 结算账号、组织资源以及管理结算访问权限。 在实操实验中，您将学习如何查看账单、使用结算报告跟踪 Google Cloud 成本、使用 BigQuery 或 Google 表格分析结算数据， 以及使用 Looker Studio 创建自定义结算信息中心。视频中提及的链接 可以在此其他资源文档中查看。

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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.

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Kubernetes 是最受欢迎的容器编排系统， Google Kubernetes Engine 专为支持 Google Cloud 中的托管式 Kubernetes 部署 而设计。在本高级课程中，您将亲自动手配置 Docker 映像、容器，并部署功能完备的 Kubernetes Engine 应用。 此课程将帮助您掌握在工作流中集成容器编排所需的 实用技能。 想要参加实操实验室挑战赛， 展示您的技能并检验所学知识？完成本课程后，不妨继续参与这项额外的 实验室挑战赛，赢得 Google Cloud 专属数字徽章。 该挑战赛位于在 Google Cloud 上部署 Kubernetes 应用课程的结尾处。

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完成在 Google Cloud 上部署 Kubernetes 应用技能徽章中级课程，展示您在以下方面的技能： 配置和构建 Docker 容器映像，创建和管理 Google Kubernetes Engine (GKE) 集群，利用 kubectl 实现高效 集群管理，以及按照稳健的持续交付 (CD) 实践部署 Kubernetes 应用。

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Google Cloud Application Programming Interfaces are the mechanism to interact with Google Cloud Services programmatically. This quest will give you hands-on practice with a variety of GCP APIs, which you will learn through working with Google’s APIs Explorer, a tool that allows you to browse APIs and run their methods interactively. By learning how to transfer data between Cloud Storage buckets, deploy Compute Engine instances, configure Dataproc clusters and much more, Exploring APIs will show you how powerful APIs are and why they are used almost exclusively by proficient GCP users. Enroll in this quest today.

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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? 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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Big data, machine learning, and scientific data? It sounds like the perfect match. In this advanced-level quest, you will get hands-on practice with GCP services like Big Query, Dataproc, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.

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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.

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完成中级技能徽章课程利用 BigQuery ML 构建预测模型时的数据工程处理， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 以及利用 BigQuery ML 构建机器学习模型。

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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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在众多课程中，本入门课程独具特色。 这些实验经过精心设计，旨在让 IT 专业人员通过实践掌握 Google Cloud 认证 Associate Cloud Engineer 考核中的各项主题和服务内容。从 IAM 到网络组建和管理， 再到 Kubernetes Engine 部署，本课程将通过特定实验 检验您的 Google Cloud 知识掌握情况。请注意，虽然这些实操 实验有助于提升您的技能和能力，我们仍建议您同时查阅 考试指南和其他可用的备考资源。

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