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The Databricks Databricks-Certified-Professional-Data-Engineer-KR exam consists of multiple-choice questions and hands-on exercises designed to test the candidate's knowledge and skills in working with Databricks. Candidates who pass the exam will be awarded the Databricks Certified Professional Data Engineer certification, which is recognized by employers worldwide as a validation of the candidate's expertise and proficiency in building and maintaining data pipelines using Databricks. Overall, the Databricks Certified Professional Data Engineer certification exam is a valuable credential for anyone looking to advance their career in big data engineering and analytics.

Databricks Certified Professional Data Engineer certification exam is a rigorous and comprehensive assessment of a candidate's knowledge and skills in data engineering. Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) certification provides a valuable credential for professionals seeking to demonstrate their expertise in designing and building data pipelines, managing data workflows, and implementing data analytics solutions using Databricks. With this certification, professionals can open up new career opportunities and demonstrate their commitment to excellence in the field of data engineering.

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

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

SectionWeightObjectives
Developing Code for Data Processing using Python and SQL22%- Integration with Databricks APIs and tools
- Data transformation and aggregation
- Batch and incremental processing logic
Data Governance7%- Data lineage and metadata tracking
- Policy enforcement
- Unity Catalog management
Data Modelling6%- Schema design and management
- Medallion Architecture implementation
- Delta Lake table design
Data Ingestion & Acquisition7%- Schema inference and evolution
- Connecting to diverse data sources
- Auto Loader and streaming ingestion
Monitoring and Alerting10%- Setting up alerts and notifications
- Performance and health monitoring
- Pipeline observability and logging
Ensuring Data Security and Compliance10%- Access control and permissions
- Compliance standards implementation
- Data encryption and masking
Debugging and Deploying10%- Deployment using bundles, CLI, and APIs
- CI/CD and DevOps practices
- Troubleshooting pipelines and errors
Data Transformation, Cleansing, and Quality10%- Data validation and quality checks
- Standardization and normalization
- Handling missing or inconsistent data
Data Sharing and Federation5%- Cross-workspace and cross-cloud access
- Unity Catalog data sharing
Cost & Performance Optimisation13%- Cluster configuration and scaling
- Storage optimization (partitioning, Z-order, indexing)
- Query optimization and caching

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