Google Cloud Professional Data Engineer Practice Tests
Take these 2 50-question practice tests that span the full scope of the Google Cloud Professional Data Engineer Certification exam to prepare yourself for one of Google Cloud's top certifications.
What can a data engineer certification do for you? The need for data engineers is constantly growing and certified data engineers are some of the top paid certified professionals. Data engineers have a wide range of skills including the ability to design systems to ingest large volumes of data, store data cost-effectively, and efficiently process and analyze data with tools ranging from reporting and visualization to machine learning. Earning a Google Cloud Professional Data Engineer certification demonstrates you have the knowledge and skills to build, tune, and monitor high-performance data engineering systems.
These exams are designed and developed by the author of the official Google Cloud Professional Data Engineer exam guide and a data architect with over 20 years of experience in databases, data architecture, and machine learning. These exams will test how well you understand how to ingest data, creating data processing pipelines, work with both relational and NoSQL databases, design highly performant Bigtable, BigQuery, and Cloud Spanner databases, query Firestore databases, and create a Spark and Hadoop cluster using Cloud Dataproc and more. You will also be tested on how to map business requirements to technical solutions, especially around problems in compliance and security.
Who this course is for:
- Anyone interested in becoming a data engineer or data analyst
Dan Sullivan is a cloud architect, systems developer, and author of the Official Google Cloud Professional Architect Study Guide, the Official Google Cloud Professional Data Engineer Study Guide, and the Official Google Cloud Associate Engineer Study Guide.
He is an experienced trainer and his online training courses have been viewed over 1 million times. Dan has extensive experience in multiple fields, including cloud architecture, data architecture and modeling, machine learning, data science, and streaming analytics.