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Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who want to demonstrate their skills and proficiency in using Databricks for data engineering tasks. Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) certification exam evaluates the candidate's ability to design and implement data pipelines, work with data sources and sinks, and perform transformations using Databricks. Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) certification can help data engineers to advance their careers and increase their earning potential, and it can also help organizations to identify and hire skilled data engineers who can design and implement data solutions using Databricks.

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Databricks Certified Professional Data Engineer certification exam is a valuable certification for data professionals who want to demonstrate their skills in building and managing complex data solutions on the Databricks platform. Databricks-Certified-Professional-Data-Engineer-KR exam is designed to test the knowledge and skills required to design, implement, and manage data engineering workflows in a collaborative environment. Candidates who pass the exam will have a deep understanding of Databricks technologies and will be able to build and manage scalable and reliable data solutions.

Reference: https://www.databricks.com/learn/certification/data-engineer-professional

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By passing the DCPDE exam, data engineers can demonstrate their proficiency in using the Databricks platform to build scalable and reliable data pipelines. Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) certification can help data engineers advance their careers and increase their earning potential by showcasing their expertise in data engineering on Databricks.

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Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Syllabus Topics:

SectionWeightObjectives
Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Jobs and job scheduling
  • 2. Task dependencies and orchestration
  • 3. Monitoring and alerting
Data Ingestion15-20%- Batch ingestion methods
  • 1. Integration with external systems
  • 2. DBR autoloader
  • 3. Spark APIs for ingestion
- Streaming ingestion
  • 1. Kafka integration
  • 2. Structured streaming fundamentals
Data Processing with Spark25-30%- Python and SQL for data engineering
  • 1. Performance optimization techniques
  • 2. Spark APIs in Python
  • 3. Built-in and user-defined functions
- Spark DataFrames and Spark SQL
  • 1. Window functions
  • 2. DataFrame operations and transformations
  • 3. Spark SQL queries and functions
Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Differences between data lake, data warehouse, and lakehouse
  • 2. Data governance fundamentals
  • 3. Bronze, silver, gold data layers
Delta Lake20-25%- Delta Lake operations
  • 1. Merge, update, delete operations
  • 2. Schema evolution and enforcement
  • 3. Delta Live Tables
- Delta Lake fundamentals
  • 1. ACID transactions
  • 2. Time travel and data versioning
  • 3. Optimize and Z-order

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