Google Cloud Professional Data Engineer Exam
Google Cloud Professional Data Engineer Exam
This learning path is designed to help you prepare for the Google Certified Professional Data Engineer Exam. Even if you don’t plan to take the exam, these courses will help you gain a solid understanding of the various data processing components of the Google Cloud Platform.
At the heart of Google’s big data services is BigQuery, a managed data warehouse in the cloud. The first three courses will show you how to load and query data in BigQuery, optimize BigQuery’s performance, and visualize your data.
The next three courses will show you how to process your data. First, you will use Cloud Machine Learning Engine to train neural networks to perform predictive analytics. Next, you’ll use Cloud Dataflow and Cloud Dataproc to build data processing pipelines that transform and summarize your data using Apache Beam, Hadoop, and Spark.
The final course will introduce you to Bigtable, Google’s revolutionary NoSQL database. It will show you how to take advantage of Bigtable’s high performance for big data applications.
All of these courses include hands-on demos you can do yourself. Then you can test what you’ve learned by taking the practice exam.
Learning Objectives
- Design a data processing system
- Build and maintain data structures and databases
- Analyze data and enable machine learning
- Optimize data representations, data infrastructure performance, and cost
- Ensure reliability of data processing infrastructure
- Visualize data
- Design secure data processing systems
Intended Audience
- Data professionals
- People studying for the Google Professional Data Engineer exam
Prerequisites
- Basic database knowledge
Professional Data Engineer
Professional Data Engineers enable data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
The Professional Data Engineer exam assesses your ability to:
- Design data processing systems
- Build and operationalize data processing systems
- Operationalize machine learning models
- Ensure solution quality
About this certification exam
Length: 2 hours
Registration fee: $200 (plus tax where applicable)
Languages: English, Japanese.
Exam format: 50-60 multiple choice and multiple select questions
Exam Delivery Method:
a. Take the online-proctored exam from a remote location
b. Take the onsite-proctored exam at a testing center
Prerequisites: None
Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.
Certification Renewal / Recertification: Candidates must recertify in order to maintain their certification status. Unless explicitly stated in the detailed exam descriptions, all Google Cloud certifications are valid for two years from the date of certification. Recertification is accomplished by retaking the exam during the recertification eligibility time period and achieving a passing score. You may attempt recertification starting 60 days prior to your certification expiration date.