Official Google Cloud Certified Professional Data Engineer Study Guide

Official Google Cloud Certified Professional Data Engineer Study Guide
PDF, ePUB
  • eBook:
    Official Google Cloud Certified Professional Data Engineer Study Guide
  • Author:
    Dan Sullivan
  • Edition:
    1 edition
  • Categories:
  • Data:
    June 10, 2020
  • ISBN:
    1119618436
  • ISBN-13:
    9781119618430
  • Language:
    English
  • Pages:
    352 pages
  • Format:
    PDF, ePUB

Book Description
The proven Study Guide that prepares you for this new Google Cloud exam
The Google Cloud Certified Professional Data Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests. 
Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data Engineer Study Guide is your ace in the hole for deploying and managing analytics and machine learning applications. 
•    Build and operationalize storage systems, pipelines, and compute infrastructure
•    Understand machine learning models and learn how to select pre-built models
•    Monitor and troubleshoot machine learning models
•    Design analytics and machine learning applications that are secure, scalable, and highly available. 

This exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform.

Content

Chapter 1. Selecting Appropriate Storage Technologies
Chapter 2. Building and Operationalizing Storage Systems
Chapter 3. Designing Data Pipelines
Chapter 4. Designing a Data Processing Solution
Chapter 5. Building and Operationalizing Processing Infrastructure
Chapter 6. Designing for Security and Compliance
Chapter 7. Designing Databases for Reliability, Scalability, and Availability
Chapter 8. Understanding Data Operations for Flexibility and Portability
Chapter 9. Deploying Machine Learning Pipelines
Chapter 10. Choosing Training and Serving Infrastructure
Chapter 11. Measuring, Monitoring, and Troubleshooting Machine Learning Models
Chapter 12. Leveraging Prebuilt Models as a Service

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