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Databricks Certified Professional Data Engineer certification exam is designed for professionals who want to prove their expertise in designing, building, and maintaining data processing systems using Databricks. Databricks is a cloud-based data platform that provides a unified analytics engine for data processing, machine learning, and visualization. Databricks Certified Professional Data Engineer Exam certification exam tests the candidate's knowledge and skills in various areas, including data engineering, data processing, data modeling, and data architecture.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is a highly sought-after certification for individuals who want to demonstrate their expertise in building reliable, scalable, and performant data pipelines using Databricks. Databricks Certified Professional Data Engineer Exam certification is designed to validate the skills and knowledge required to design, implement, and maintain data pipelines for big data processing using Databricks.
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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
| Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Data Transformation, Cleansing, and Quality | 10% | - Handling missing or inconsistent data - Data validation and quality checks - Standardization and normalization |
| Ensuring Data Security and Compliance | 10% | - Access control and permissions - Data encryption and masking - Compliance standards implementation |
| 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 |
| Debugging and Deploying | 10% | - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices |
| Data Modelling | 6% | - Delta Lake table design - Schema design and management - Medallion Architecture implementation |
| Monitoring and Alerting | 10% | - Setting up alerts and notifications - Performance and health monitoring - Pipeline observability and logging |
| Data Governance | 7% | - Data lineage and metadata tracking - Unity Catalog management - Policy enforcement |
| Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching |
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