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To become a Databricks Certified Professional Data Engineer, candidates must pass a rigorous exam that covers a wide range of topics related to data engineering. Databricks-Certified-Professional-Data-Engineer-KR exam is designed to test a candidate's ability to design, build, and maintain data pipelines using Databricks tools and technologies. It also tests their understanding of data modeling, data warehousing, and data integration.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
The Databricks Databricks-Certified-Professional-Data-Engineer-KR exam is designed to assess the proficiency of the candidates in various areas related to data engineering on Databricks. Databricks-Certified-Professional-Data-Engineer-KR exam focuses on topics such as data ingestion, data transformation, data modeling, data storage, and data processing. Databricks-Certified-Professional-Data-Engineer-KR exam tests the candidates' knowledge of using Databricks to build data pipelines that can handle large volumes of data, process data in real-time, and integrate with other data sources.
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Databricks Certified Professional Data Engineer is a certification exam that measures individuals' knowledge and skills in using Databricks to manipulate big data. Databricks is a cloud-based data processing platform that allows data engineers to build, deploy, and manage big data processing pipelines. Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) certification exam is designed to validate the expertise of data engineers who work with Databricks.
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Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Security, Governance, Monitoring, and Optimization | - Implement Unity Catalog governance and access control - Cost optimization and performance tuning - Monitor and optimize Spark workloads |
| Data Modeling and Storage | - Delta Lake table design and optimization - Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures |
| Data Ingestion and Transformation | - Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks |
| Production Pipelines and Orchestration | - Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance |
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