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Preparing for the Google Cloud Professional Data Engineer Exam

Approx. 9 hours to complete

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Course Summary

This course prepares you for the Google Cloud Professional Data Engineer Exam, covering topics such as designing and building data processing systems, operationalizing machine learning models, and ensuring data security.

Key Learning Points

Learn how to design and implement data processing systems on Google Cloud Platform
Master the skills to operationalize machine learning models
Understand how to ensure data security and compliance

Learning Outcomes

Design and implement data processing systems on Google Cloud Platform
Operationalize machine learning models
Ensure data security and compliance

Prerequisites or good to have knowledge before taking this course

Experience with data processing systems and machine learning models
Familiarity with cloud computing

Course Difficulty Level

Intermediate

Course Format

Self-paced online course
Video lectures
Hands-on labs

Similar Courses

Architecting with Google Compute Engine
Data Engineering on Google Cloud Platform
Google Cloud Platform Fundamentals: Core Infrastructure

Related Education Paths

Related Books

Description

The purpose of this course is to help those who are qualified develop confidence to attempt the exam, and to help those not yet qualified to develop their own plan for preparation.

Outline

Understanding the Professional Data Engineer Certification
Introduction
Understanding the Professional Data Engineer certification
Getting Started with Google Cloud and Qwiklabs
Information about the latest Exam Guide
How to Send Feedback
Exam Tips #1
Exam Tips #2
Module 1 Assessment

Designing Data Processing Systems
Designing and building
Designing flexible data representations
Design data pipelines
Dataflow pipelines
BigQuery and Dataflow Solutions
Design data processing infrastructure
Pub/Sub solutions
Practice exam questions 01
Exam Tips #3
Designing Data Processing Systems: Exam Guide Review

Building and Operationalizing Data Processing Systems
Building data processing systems
Cloud Storage, Cloud SQL, Cloud Bigtable
Building and maintaining pipelines
Building and maintaining processing infrastructure
Practice exam questions 02
Case study 01
Challenge Lab 01 Intro
Building and Operationalizing Data Processing Systems: Exam Guide Review
Case Study 1
Exam Tips #4
Designing and Building Data Processing Systems

Operationalizing Machine Learning Models
Analyzing and modeling
Analyzing data
Machine learning
Machine learning and unstructured data
Training and validating
Practice exam questions 03
Case study 02
Modeling business processes for analysis and optimization
Feature engineering and performance
Schema and performance
Pipeline and performance
Dividing work
Bigtable performance
Price estimation
Case study 03
Challenge Lab 02 Intro
Operationalizing Machine Learning Models: Exam Guide Review
Case Study 2
Case Study 3
Exam Tips #5
Operationalizing Machine Learning Models

Security, Policy, and Reliability
Designing for Security and Compliance
Practice exam questions 04
Performing quality control
Ensuring reliability
Data visualization and reporting tools
Practice exam questions 05
Ensuring Solution Quality: Exam Guide Review
Exam Tips #6
Preparing for Reliability, Policy, and Security

Resources and Next Steps
Resources and next steps
Practice exam quiz
Resources
Instructions of GRADED and UNGRADED Practice Exam Quizzes
Exam Tips #7
Sample Exam Quiz Questions
Ungraded Practice Exam
Graded Practice Exam
Course Summary Quiz - Prep for the Exam

Summary of User Reviews

Read reviews on Preparing for the Cloud Professional Data Engineer Exam course on Coursera. Many users found the course to be comprehensive and helpful in preparing for the exam. Overall, the course has received positive feedback from users.

Key Aspect Users Liked About This Course

Comprehensive content

Pros from User Reviews

Well-structured course material
Good coverage of exam topics
In-depth explanations of concepts

Cons from User Reviews

Some users found the pace of the course to be too slow
Lack of interaction with instructors
Limited opportunities for hands-on practice

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