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Rahul V

Учасник із 2020

Золота ліга

Кількість балів: 12412
Introduction to AI Agents Earned серп. 18, 2026 EDT
Agent Fundamentals Earned черв. 17, 2026 EDT
Create ML Models with BigQuery ML Earned бер. 31, 2021 EDT
DEPRECATED Windows on Google Cloud Earned січ. 13, 2021 EST
VM Migration Earned лист. 24, 2020 EST
DEPRECATED Websites and Web Applications Earned лист. 4, 2020 EST
Using the Cloud SDK Command Line Earned жовт. 18, 2020 EDT
NCAA® March Madness®: Bracketology with Google Cloud Earned жовт. 16, 2020 EDT
DEPRECATED BigQuery for Marketing Analysts Earned жовт. 15, 2020 EDT
Data Catalog Fundamentals Earned жовт. 15, 2020 EDT
Cloud SQL Earned жовт. 15, 2020 EDT
Security & Identity Fundamentals Earned жовт. 7, 2020 EDT
Baseline: Deploy & Develop Earned жовт. 7, 2020 EDT
DEPRECATED BigQuery for Data Warehousing Earned вер. 22, 2020 EDT
Optimize Your Google Cloud Costs Earned вер. 11, 2020 EDT
Understand Your Google Cloud Costs Earned вер. 9, 2020 EDT
BigQuery for Machine Learning Earned вер. 3, 2020 EDT
Deploy Kubernetes Applications on Google Cloud Earned серп. 28, 2020 EDT
Kubernetes in Google Cloud Earned серп. 26, 2020 EDT
Engineer Data for Predictive Modeling with BigQuery ML Earned серп. 26, 2020 EDT
[DEPRECATED] Data Engineering Earned серп. 26, 2020 EDT
Build a Website on Google Cloud Earned серп. 18, 2020 EDT
Derive Insights from BigQuery Data Earned серп. 13, 2020 EDT
Підготовка даних для інтерфейсів API машинного навчання в Google Cloud Earned серп. 3, 2020 EDT
Початок роботи з даними, машинним навчанням і штучним інтелектом Earned лип. 30, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts Earned лип. 30, 2020 EDT
Розробка хмарних технологій Earned лип. 29, 2020 EDT
Початок роботи з інфраструктурою Earned лип. 29, 2020 EDT
DEPRECATED Cloud Architecture Earned лип. 29, 2020 EDT
Set Up a Google Cloud Network Earned лип. 29, 2020 EDT
Налаштування середовища для розробки додатка в Google Cloud Earned лип. 28, 2020 EDT
Building Codeless Pipelines on Cloud Data Fusion Earned лип. 27, 2020 EDT
Основи Google Cloud Earned лип. 27, 2020 EDT
DEPRECATED BigQuery for Data Analysis Earned лип. 24, 2020 EDT
Налаштування мережі Google Cloud Earned лип. 24, 2020 EDT
Cloud Architecture: Design, Implement, and Manage Earned лип. 22, 2020 EDT
Налаштування Cloud Load Balancing для Compute Engine Earned лип. 16, 2020 EDT

Gain a conceptual overview of AI Agents. Discover how AI Agents use autonomous action and reasoning to solve complex problems. You’ll explore the technical architecture—models, tools, and orchestration—that enables agents to learn, plan, and achieve goals on your behalf.

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This course introduces the fundamentals of AI agents and explores where agents add value in the real world. It provides a foundation for developers, architects, and technical decision-makers who want to understand AI systems through the lens of autonomous, goal-directed behavior. Explore other content in the Gemini Agent Ready (GEAR) program.

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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.

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Google Cloud is committed to supporting Windows workloads in its frameworks and services. In this advanced-level quest, you will get hands-on practice running many of the popular Windows services on Google Cloud. For example, you will learn how to instantiate Microsoft SQL databases, cloud tools for Powershell on Google Cloud Platform frameworks.

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Google Cloud’s four step structured Cloud Migration Path Methodology provides a defined and repeatable path for users to follow when migrating and modernizing Virtual Machines. In this quest, you will get hands-on practice with Google’s current solution set for VM assessment, planning, migration, and modernization. You will start by analyzing your lab environment and building assessment reports with CloudPhysics and StratoZone, then build a landing zone within Google Cloud leveraging Terraform’s infrastructure-as-code templates, next you will manually transform a two-tier application into a cloud-native workload running on Kubernetes, and finally, transform a VM workload into Kubernetes with Migrate for Anthos and migrate a VM between cloud environments.

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When it comes to hosting websites and web applications, you want a framework that’s robust, fast, and secure. By choosing the Google Cloud Platform, you will have all of those needs covered. In this fundamental-level quest, you will get hands-on practice with GCPs key infrastructure and computing services for the web. From deploying your first web app, to integrating Cloud SQL with Ruby on Rails, to mapping the NYC subway system on App Engine, you will learn all the skills needed to harness GCPs web hosting power.

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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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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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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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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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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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Security is an uncompromising feature of Google Cloud services, and Google Cloud has developed specific tools for ensuring safety and identity across your projects. In this fundamental-level quest, you will get hands-on practice with Google Cloud’s Identity and Access Management (IAM) service, which is the go-to for managing user and virtual machine accounts. You will get experience with network security by provisioning VPCs and VPNs, and learn what tools are available for security threat and data loss protections.

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In this introductory-level quest, you will learn the fundamentals of developing and deploying applications on the Google Cloud Platform. You will get hands-on experience with the Google App Engine framework by launching applications written in languages like Python, Ruby, and Java (just to name a few). You will see first-hand how straightforward and powerful GCP application frameworks are, and how easily they integrate with GCP database, data-loss prevention, and security services.

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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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This is the second Quest in a two-part series on Google Cloud billing and cost management essentials. This Quest is most suitable for those in a Finance and/or IT related role responsible for optimizing their organization’s cloud infrastructure. Here you'll learn several ways to control and optimize your Google Cloud costs, including setting up budgets and alerts, managing quota limits, and taking advantage of committed use discounts. In the hands-on labs, you’ll practice using various tools to control and optimize your Google Cloud costs or to influence your technology teams to apply the cost optimization best practices.

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This course is suitable for technology or finance roles managing Google Cloud costs. Learn to set up a billing account, organize resources, and manage access permissions. In hands-on labs, you view invoices and analyze billing data with BigQuery. You also use Google Sheets and create dashboards with Data Studio. You find references from the videos in this Additional Resources document.

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Want to build ML models in minutes instead of hours using just SQL? BigQuery ML democratizes machine learning by letting data analysts create, train, evaluate, and predict with machine learning models using existing SQL tools and skills. In this series of labs, you will experiment with different model types and learn what makes a good model.

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Complete the intermediate Deploy Kubernetes Applications on Google Cloud skill badge course to demonstrate skills in the following: Configuring and building Docker container images.Creating and managing Google Kubernetes Engine (GKE) clusters.Utilizing kubectl for efficient cluster management.Deploying Kubernetes applications with robust continuous delivery (CD) practices.

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Kubernetes is the most popular container orchestration system, and Google Kubernetes Engine was designed specifically to support managed Kubernetes deployments in Google Cloud. In this course, you will get hands-on practice configuring Docker images, containers, and deploying fully-fledged Kubernetes Engine applications.

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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.

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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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Earn the introductory skill badge by completing the Build a Website on Google Cloud skill badge course. This course is based on the Get Cooking in Cloud series and covers`:`Deploying a website on Cloud RunHosting a web app on Compute EngineCreating, deploying, and scaling your website on Google Kubernetes EngineMigrating from a monolithic application to a microservices architecture using Cloud Build

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Complete the introductory Derive Insights from BigQuery Data skill badge course to demonstrate skills in the following: Write SQL queries.Query public tables.Load sample data into BigQuery.Troubleshoot common syntax errors with the query validator in BigQuery.Create reports in Data Studio by connecting to BigQuery data.

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Пройдіть вступний кваліфікаційний курс Підготовка даних для інтерфейсів API машинного навчання в Google Cloud, щоб продемонструвати свої навички щодо очистки даних за допомогою сервісу Dataprep by Trifacta, запуску конвеєрів даних у Dataflow, створення кластерів і запуску завдань Apache Spark у Managed Service for Apache Spark, а також виклику API машинного навчання, зокрема Cloud Natural Language API, Google Cloud Speech-to-Text API і Video Intelligence API.

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Зараз усі говорять про масиви даних, машинне навчання й штучний інтелект, але це досить вузькоспеціалізовані теми, про які важко знайти матеріали, зрозумілі не лише спеціалістам. На щастя, Google Cloud пропонує зручні сервіси в цих галузях, а завдяки цьому вступному курсу ви зможете ознайомитися з такими інструментами, як BigQuery, Cloud Speech API і Video Intelligence.

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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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Цей вступний курс унікальний серед інших курсів. Практичні заняття розроблено так, щоб ІТ-фахівці могли опрацювати теми й сервіси, які ввійдуть до сертифікації Google Cloud Certified Associate Cloud Engineer. У цей курс входять спеціально розроблені практичні заняття, зокрема присвячені керуванню ідентифікацією і доступом, організації мереж і розгортанню Kubernetes Engine, щоб ви могли перевірити свої знання про Google Cloud. Майте на увазі, що хоча виконання цих практичних занять покращить ваші навички й уміння, радимо також переглянути посібник із підготовки до іспиту й інші доступні ресурси.

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Якщо ви лише пробуєте розробляти хмарні рішення й шукаєте практичні заняття на додаток до кваліфікаційного курсу "Знайомство з Google Cloud", тоді цей курс саме для вас. Ви отримаєте прикладний досвід завдяки практичним заняттям, присвяченим Cloud Storage і іншим ключовим сервісам додатків, як-от Monitoring і Cloud Functions. Ви отримаєте цінні навички, які можна застосовувати в будь-яких проєктах Google Cloud.

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This fundamental-level quest is unique amongst the other quest 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 Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.

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Earn a skill badge by completing the Set Up a Google Cloud Network skill badge course, where you will learn how to perform basic networking tasks on Google Cloud Platform - create a custom network, add subnets firewall rules, then create VMs and test the latency when they communicate with each other.

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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування середовища для розробки додатка в Google Cloud. У ньому ви навчитеся створювати й підключати хмарну інфраструктуру, спрямовану на зберігання даних, за допомогою базових можливостей таких технологій, як Cloud Storage, система керування ідентифікацією і доступом, Cloud Functions та Pub/Sub.

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This course offers hands-on practice with Cloud Data Fusion, a cloud-native, code-free, data integration platform. ETL Developers, Data Engineers and Analysts can greatly benefit from the pre-built transformations and connectors to build and deploy their pipelines without worrying about writing code. This Quest starts with a quickstart lab that familiarises learners with the Cloud Data Fusion UI. Learners then get to try running batch and realtime pipelines as well as using the built-in Wrangler plugin to perform some interesting transformations on data.

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На цьому вступному курсі ви отримаєте практичний досвід роботи з основними інструментами й сервісами Google Cloud. Також вам доступні додаткові відео, які допоможуть краще зрозуміти контекст і закріпити знання щодо тем, розглянутих у практичних заняттях. Google Cloud "Основи" – це рекомендований початковий курс для тих, хто починає вивчати Google Cloud. Ви можете розпочати навчання, маючи мінімальні знання про хмарні технології або взагалі не маючи їх, і отримати практичний досвід, який зможете застосувати у своєму першому хмарному проєкті Google Cloud. "Основи Google Cloud" дають вичерпне уявленні про ключові функції платформи – від написання команд у Cloud Shell і розгортання першої віртуальної машини до запуску додатків на Kubernetes Engine і використання розподілу навантаження.

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Want to learn the core SQL and visualization skills of a Data Analyst? Interested in how to write queries that scale to petabyte-size datasets? Take the BigQuery for Analyst Quest and learn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.

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Щоб отримати кваліфікаційний значок, пройдіть курс Налаштування мережі Google Cloud. У ньому ви дізнаєтеся про різні способи розгортання й моніторингу додатків, зокрема навчитеся визначати ролі керування ідентифікацією і доступом, надавати або вилучати доступ до проектів, створювати мережі VPC, розгортати й відстежувати віртуальні машини Compute Engine, писати запити SQL, а також по-різному вводити додатки в дію за допомогою Kubernetes.

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Earn a skill badge by completing the Cloud Architecture: Design, Implement, and Manage to demonstrate skills in the following: deploy a publicly accessible website using Apache web servers, configure a Compute Engine VM using startup scripts, configure secure RDP using a Windows Bastion host and firewall rules, build and deploy a Docker image to a Kubernetes cluster and then update it, and create a CloudSQL instance and import a MySQL database. This skill badge is a great resource for understanding topics that will appear in the Google Cloud Certified Professional Cloud Architect certification exam.

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Пройдіть вступний кваліфікаційний курс Налаштування Cloud Load Balancing для Compute Engine, щоб продемонструвати свої навички: створення й розгортання віртуальних машин у Compute Engine; налаштування мережі й розподілювачів навантаження додатків.

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