Preparing for the Google Cloud Professional Data Engineer Exam
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.
What you'll learn
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.
Table of contents
- Designing and building 10m
- Design flexible data representations 10m
- Exam Tips #3 0m
- Design data pipelines 5m
- Dataflow pipelines 3m
- BigQuery and Dataflow solutions 3m
- Design data processing infrastructure 2m
- Pub/Sub solutions 2m
- Practice exam questions 01 2m
- Designing Data Processing Systems: Exam Guide Review 0m
- Building data processing systems 2m
- Cloud Storage, Cloud SQL, Cloud Bigtable 3m
- Building and maintaining pipelines 2m
- Building and maintaining processing infrastructure 1m
- Practice exam questions 02 2m
- Building and Operationalizing Data Processing Systems: Exam Guide Review 0m
- Case study 01 4m
- Case Study 1 0m
- Challenge Lab 01 Intro 0m
- Pluralsight: Getting Started with Google Cloud and Qwiklabs 4m
- Lab: PDE Prep: BigQuery Essentials 0m
- Exam Tips #4 0m
- Analyzing and modeling 3m
- Analyzing data 3m
- Machine learning 1m
- Machine learning and unstructured data 5m
- Training and validating 2m
- Practice exam questions 03 3m
- Operationalizing Machine Learning Models: Exam Guide Review 0m
- Case study 02 4m
- Case Study 2 0m
- Modeling business processes for analysis and optimization 2m
- Feature engineering and performance 2m
- Schema and performance 2m
- Pipeline and performance 2m
- Dividing work 2m
- Bigtable performance 4m
- Price estimation 1m
- Case study 03 3m
- Case Study 3 0m
- Challenge Lab 02 Intro 0m
- Lab: PDE Prep : Cloud Dataproc Cluster Operations and Maintenance 0m
- Exam Tips #5 0m