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Data Engineering

Google Professional Data Engineer

Google Cloud's professional data engineer certification validates expertise in designing and implementing data processing systems on GCP. Covers Dataflow, BigQuery, and Pub/Sub.

Provider

Google Cloud

Level

Professional

Duration

2–4 months

Cost

$200

Passing Score

70%

Validity

2 years

Salary Impact

+$18,000–$25,000 avg

Prerequisites

GCP fundamentals

Python or Java

SQL and data warehousing concepts

Exam Format

Multiple choice and scenario-based questions, 120 minutes, $200 USD

Exam Topics & Weights

Design scalable data processing systems

22%

Build and maintain data pipelines

25%

Operate and secure data systems

21%

Analyze data and validate data quality

19%

Visualize data and implement machine learning models

13%

8-Week Study Plan

Weeks 1-2

Focus: GCP data engineering services overview and Pub/Sub

Google Cloud Skills Boost Data Engineer path

Pub/Sub documentation

Event-driven architectures

Weeks 3-4

Focus: BigQuery: data warehouse, SQL, and optimization

BigQuery fundamentals

BigQuery best practices

Query optimization and performance tuning

Weeks 5-6

Focus: Dataflow and Apache Beam for data pipelines

Apache Beam programming model

Dataflow for batch and streaming

Advanced pipeline patterns

Weeks 7-8

Focus: Data security, governance, and quality

Cloud IAM for data access

Data Catalog for metadata

Data loss prevention and encryption

Week 9

Focus: Practice exams and case studies

Google official practice exam

Real GCP data engineering case studies

Lab walkthroughs

Exam Tips & Strategies

BigQuery is at the heart of GCP data engineering. Understand partitioning, clustering, and query optimization.

Dataflow and Apache Beam are critical for streaming pipelines. Understand windowing and state management.

Study real-world GCP case studies—the exam includes scenario-based questions

Know the differences between batch and streaming approaches and when to use each

Get hands-on with GCP—design and build pipelines using free credits

Understand data quality patterns and how to validate data in pipelines

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