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Databricks Certified Professional Data Engineer exam covers a wide range of topics, including data engineering concepts, Databricks architecture, data ingestion and processing, data storage and management, and data security. Databricks-Certified-Professional-Data-Engineer-KR exam consists of 60 multiple-choice questions and participants have 90 minutes to complete it. Passing the exam requires a score of 70% or higher, and successful candidates receive a certificate that validates their expertise in building and managing data pipelines on the Databricks platform.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
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The DCPDE certification is an excellent way for data professionals to demonstrate their expertise in the Databricks platform. Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) certification is recognized globally and is highly valued by employers looking for data professionals with expertise in Databricks. The DCPDE certification provides professionals with the opportunity to enhance their career prospects and increase their earning potential.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance | 7% | - Unity Catalog management - Policy enforcement - Data lineage and metadata tracking |
| Topic 2: Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data |
| Topic 3: Ensuring Data Security and Compliance | 10% | - Access control and permissions - Compliance standards implementation - Data encryption and masking |
| Topic 4: Developing Code for Data Processing using Python and SQL | 22% | - Integration with Databricks APIs and tools - Data transformation and aggregation - Batch and incremental processing logic |
| Topic 5: Monitoring and Alerting | 10% | - Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications |
| Topic 6: Cost & Performance Optimisation | 13% | - Query optimization and caching - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) |
| Topic 7: Debugging and Deploying | 10% | - Troubleshooting pipelines and errors - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs |
| Topic 8: Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Schema inference and evolution - Auto Loader and streaming ingestion |
| Topic 9: Data Modelling | 6% | - Schema design and management - Medallion Architecture implementation - Delta Lake table design |
| Topic 10: Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
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