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