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Quest

Desafio: arquitetura do GCP

Esta Quest de laboratórios com desafios oferece um treinamento prático em soluções comuns de negócios/tecnologia que usam arquiteturas do GCP, servindo como preparação para a certificação Google Cloud Certified Professional Cloud Architect. Em vez de apresentar um manual passo a passo, os desafios fornecem ao estudante um mínimo de orientação para a criação de soluções relacionadas a várias tecnologias do GCP. Todos os laboratórios têm acompanhamento das atividades e, para conquistar o selo, você precisa atingir uma pontuação de 100% em todos eles. Esta Quest não é nada fácil e colocará à prova seu conhecimento sobre a tecnologia do GCP. Os laboratórios ajudarão a desenvolver suas habilidades. No entanto, recomendamos que você também estude outros materiais e aumente sua experiência com arquitetura de nuvem como preparação para a certificação.

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Quest

Engenharia de dados

Esta Quest de nível avançado é exclusiva entre as outras ofertas do Qwiklabs. Os laboratórios foram criados para oferecer um treinamento prático aos profissionais de TI nos tópicos e serviços da certificação Google Cloud Certified Professional Data Engineer. Abrangendo desde o Big Query até o Dataproc e o TensorFlow, esta Quest é composta de laboratórios específicos que colocarão à prova seu conhecimento de engenharia de dados do GCP. Os laboratórios ajudarão a desenvolver suas habilidades, mas será necessário se preparar mais. O exame é difícil, por isso recomendamos que você estude materiais externos, além de ter experiência com engenharia de dados na nuvem.

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Quest

Exploring APIs

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.

Quest

Data Science on the Google Cloud Platform

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 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 Platform tools and services.

Quest

Data Science on Google Cloud Platform: Machine Learning

This is the second of two Quests of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud Platform tools and services.

Quest

IoT in the Google Cloud

In this quest, you will learn about Google Cloud’s IoT Core service and its integration with other services like GCS, Dataprep, Stackdriver and Firestore. The labs in this quest use simulator code to mimic IOT devices and the learning here should empower you to implement the same streaming pipeline with real world IoT devices.

Quest

G Suite: Integrations

This Quest of hands-on labs demonstrates the power of integrating Google Cloud Platform services and tools with G Suite applications. With integration technologies such as App Script and the Clasp Command Line environment, you will create and publish web apps and add-ons for G Suite products: Sheets, Docs, Forms, and Slides. With App Maker you will build a ready-to-use app that has a Google Cloud SQL Database, Google Maps integration, and a Mobile Responsive Design. Other labs create direct connections to GCP data sources-- using the BigQuery API, Sheets, and Slides to collect, analyze and present data.

Quest

App Modernization with Apigee

Apigee enables you to create APIs and manage them for the benefit of other developers who might need to use your software. Apigee Edge enables you to quickly expose backend services as APIs. These "API Products" offer different capabilities and levels of service, with consumption managed by Apigee. Istio is an open source framework for connecting, securing, and managing microservices, especially services that are hosted in a Kubernetes cluster. This Quest of hands-on labs gives you practice in using Apigee for API creation and management functionality when you decide to modernize an application backend on Google Kubernetes Engine and an Istio based service mesh.

Quest

Kubernetes Solutions

Containerized applications have changed the game and are here to stay. With Kubernetes, you can orchestrate containers with ease, and integration with the Google Cloud Platform is seamless. In this advanced-level quest, you will be exposed to a wide range of Kubernetes use cases and will get hands-on practice architecting solutions over the course of 9 labs. From building Slackbots with NodeJS, to deploying game servers on clusters, to running the Cloud Vision API, Kubernetes Solutions will show you first-hand how agile and powerful this container orchestration system is.

Quest

Developing Data and Machine Learning Apps with C#

C# has powered Windows .NET application development for nearly two decades and Google Cloud is committed to supporting developers getting their .NET workloads up and running on the GCP platform. In this quest, you will learn how to run C# apps in GCP, and specifically how to take your apps to the next level by interfacing them with the big data and machine learning APIs that are accessible now from C#. By enrolling in Developing Data and Machine Learning Apps with C# you will see firsthand how seamlessly GCP integrates with .NET workloads and what the possibilities are for leveraging big data and ML services in your own C# projects.

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