Coursera

Preparing for the Google Cloud Professional Data Engineer Exam em Português Brasileiro (Coursera)

Offered by Google Cloud,
Preparing for the Google Cloud Professional Data Engineer Exam em Português Brasileiro (Coursera)

Por que fazer o curso: "A melhor forma de se preparar para o exame é ser competente nas habilidades necessárias ao trabalho." Este curso usa uma abordagem "top-down". Ele identifica as habilidades que você já tem e apresenta novas informações e áreas para ampliar seus conhecimentos. Use este curso para criar seu plano de preparação personalizado. Ele ajudará você a identificar o que sabe e o que precisa estudar mais, além de desenvolver e praticar as habilidades necessárias às competências do cargo.

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O curso segue a metodologia do Guia do exame. Ele apresenta conceitos mais abrangentes para você avaliar seu conhecimento na área e saber se precisa estudar mais. Você também vai aprender e praticar habilidades essenciais ao trabalho, como analisar casos, identificar pontos de controle técnicos e desenvolver soluções propostas. Essas habilidades também serão avaliadas no exame. Nos laboratórios com desafio, você colocará em prática seus conhecimentos básicos. Além disso, você verá exemplos de perguntas e soluções parecidas com as do exame. Ao final do curso, haverá um teste sem nota e depois outro com nota que simulam a experiência real do exame.

What You Will Learn

  • Review each section of the exam using highest-level concepts to identify what is already known and surface gap areas for study.
  • Practice case study analysis and solution proposal methods and thinking skills.
  • Learn information, tips, and general advice about how to prepare for the exam.
  • Integrate prior technical skills into practical skills for the job role. Help you become a Data Engineer.

Syllabus

WEEK 1
Este é o curso "Preparing for the Professional Data Engineer Exam"
Descrição do módulo.
Como projetar sistemas de processamento de dados
Como criar e operacionalizar sistemas de processamento de dados
Como operacionalizar modelos de machine learning
Confiabilidade, política e segurança para garantir a qualidade da solução
Recursos e próximas etapas

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