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Databricks-Certified-Professional-Data-Engineer certification exam is a comprehensive test that covers all aspects of data engineering with Databricks. Databricks-Certified-Professional-Data-Engineer exam is designed to test the candidate's knowledge of Databricks architecture, data engineering concepts, data processing with Databricks, and data storage with Databricks. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's ability to design, implement, and maintain data engineering solutions using Databricks.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
Databricks-Certified-Professional-Data-Engineer certification exam is a comprehensive test that covers all aspects of data engineering with Databricks. Databricks-Certified-Professional-Data-Engineer exam is challenging and requires a comprehensive understanding of data engineering concepts and Databricks technology. Databricks Certified Professional Data Engineer Exam certification is a valuable credential for data engineers who work with Databricks and demonstrates the candidate's expertise in Databricks technology and data engineering concepts.
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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modelling | 6% | - Schema design and management - Medallion Architecture implementation - Delta Lake table design |
| Topic 2: Monitoring and Alerting | 10% | - Performance and health monitoring - Setting up alerts and notifications - Pipeline observability and logging |
| Topic 3: Data Transformation, Cleansing, and Quality | 10% | - Handling missing or inconsistent data - Data validation and quality checks - Standardization and normalization |
| Topic 4: Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors - CI/CD and DevOps practices |
| Topic 5: Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Topic 6: Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Data encryption and masking - Access control and permissions |
| Topic 7: Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Auto Loader and streaming ingestion - Connecting to diverse data sources |
| Topic 8: Data Governance | 7% | - Unity Catalog management - Data lineage and metadata tracking - Policy enforcement |
| Topic 9: Developing Code for Data Processing using Python and SQL | 22% | - Data transformation and aggregation - Batch and incremental processing logic - Integration with Databricks APIs and tools |
| Topic 10: Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching |
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