Exam Topics

The syllabus of the Google Professional Data Engineer exam is divided into 4 topics, each covering specific knowledge and skills that the candidates need to develop while preparing for the test. A full outline of the exam content can be viewed on the official website. The highlights of the domains covered in the test are as follows:

Topic 1. Designing Data Processing Systems

To answer the questions related to this first topic of the certification exam, the individuals need to demonstrate their proficiency in selecting the proper storage technologies. This includes their understanding of data modeling, schema design, distributed systems, as well as tradeoffs involving throughput, latency, and transactions. Moreover, the applicants need to have the ability to map storage systems to the business needs. It also measures one’s skills in designing data pipelines, designing a data processing solution, as well as migrating data warehousing & data processing.

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What is the duration, language, and format of Google Professional Data Engineer Exam

• Language: English (U.S.), Japanese, Spanish, and Portuguese
• Length of Examination: 120 minutes
• Passing score: 80%
• Number of Questions: 50-60
• Cost: $200
• Format: Multiple choices, multiple answers

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Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

• Enable insights from streaming data
• Designing and building data processing systems on the Google Cloud Platform
• Predicting machine models using TensorFlow and Cloud ML
• Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
• Influencing unstructured data using ML APIs on Cloud Dataproc

Reference: https://cloud.google.com/certification/data-engineer

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