Official Google Cloud Certified Professional Data Engineer Study Guide
- 7h 19m
- Dan Sullivan
- Sybex
- 2020
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.
About the Author
DAN SULLIVAN is a software architect specializing in data architecture, machine learning, and cloud computing. Dan is a Google Cloud Certified Professional Data Engineer, Professional Architect, and Associate Cloud Engineer. Dan is the author of six books and numerous articles. He is an instructor with LinkedIn Learning and Udemy for Business.
In this Book
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Introduction
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Assessment Test
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Selecting Appropriate Storage Technologies
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Building and Operationalizing Storage Systems
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Designing Data Pipelines
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Designing a Data Processing Solution
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Building and Operationalizing Processing Infrastructure
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Designing for Security and Compliance
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Designing Databases for Reliability, Scalability, and Availability
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Understanding Data Operations for Flexibility and Portability
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Deploying Machine Learning Pipelines
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Choosing Training and Serving Infrastructure
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Measuring, Monitoring, and Troubleshooting Machine Learning Models
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Leveraging Prebuilt Models as a Service
