Last Updated: Aug 26, 2026
No. of Questions: 433 Questions & Answers with Testing Engine
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There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.
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The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.
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.
Reference: https://cloud.google.com/certification/data-engineer
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalizing machine learning models | 26% | - Model deployment and monitoring
|
| Topic 2: Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Topic 3: Designing data processing systems | 22% | - Data architecture and storage design
|
| Topic 4: Ensuring solution quality | 28% | - Security and governance
|
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