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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) exam is a certification program designed to validate the skills and expertise of data engineers in developing and managing big data pipelines using Databricks. Databricks-Certified-Professional-Data-Engineer-KR exam is ideal for data engineers, ETL developers, and data architects who work with Databricks and want to showcase their skills and proficiency.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
| Topic 2: Data Modelling | 6% | - Schema design and management - Medallion Architecture implementation - Delta Lake table design |
| Topic 3: Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Topic 4: Data Governance | 7% | - Unity Catalog management - Policy enforcement - Data lineage and metadata tracking |
| Topic 5: Cost & Performance Optimisation | 13% | - Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling |
| Topic 6: Developing Code for Data Processing using Python and SQL | 22% | - Batch and incremental processing logic - Data transformation and aggregation - Integration with Databricks APIs and tools |
| Topic 7: Data Transformation, Cleansing, and Quality | 10% | - Standardization and normalization - Data validation and quality checks - Handling missing or inconsistent data |
| Topic 8: Ensuring Data Security and Compliance | 10% | - Access control and permissions - Compliance standards implementation - Data encryption and masking |
| Topic 9: Monitoring and Alerting | 10% | - Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring |
| Topic 10: Debugging and Deploying | 10% | - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors |
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