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Databricks Certified Professional Data Engineer certification is recognized by leading companies in the tech industry as a benchmark of excellence in the field of data engineering. It is a valuable credential for individuals who are looking to advance their careers in this field and are seeking to demonstrate their expertise in Databricks technologies. Additionally, companies that employ Databricks Certified Professional Data Engineers can be assured that they have the necessary skills to build and maintain complex data pipelines and analytics systems.
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Databricks Certified Professional Data Engineer certification exam is a hands-on exam that requires candidates to demonstrate their skills in building data pipelines and workflows using Databricks. Databricks-Certified-Professional-Data-Engineer-KR exam consists of a set of performance-based tasks that require candidates to design, implement, and manage data solutions in a Databricks environment. Candidates are given a set of data engineering scenarios and must use Databricks to build solutions that meet the requirements of each scenario.
Databricks is a cloud-based platform that provides data engineering, machine learning, and analytics services. The Databricks Certified Professional Data Engineer (DCPDE) exam is designed to test the skills and knowledge of data engineers who are responsible for building data pipelines, managing data infrastructure, and optimizing data processing workflows on the Databricks platform.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Production Pipelines and Orchestration | - Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs |
| Data Modeling and Storage | - Schema evolution and data partitioning strategies - Delta Lake table design and optimization - Design scalable data lakehouse architectures |
| Security, Governance, Monitoring, and Optimization | - Implement Unity Catalog governance and access control - Cost optimization and performance tuning - Monitor and optimize Spark workloads |
| Data Ingestion and Transformation | - Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines |







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