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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.

Databricks Certified Professional Data Engineer exam is designed to test the knowledge and skills of data professionals who use Databricks for data engineering tasks. Databricks-Certified-Professional-Data-Engineer exam covers a range of topics, including data ingestion, data transformation, data storage, and data analysis. Databricks-Certified-Professional-Data-Engineer exam also tests candidates' ability to use Databricks tools and services to perform these tasks effectively.

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Modelling6%- Schema design and management
- Medallion Architecture implementation
- Delta Lake table design
Topic 2: Monitoring and Alerting10%- Performance and health monitoring
- Setting up alerts and notifications
- Pipeline observability and logging
Topic 3: Data Transformation, Cleansing, and Quality10%- Handling missing or inconsistent data
- Data validation and quality checks
- Standardization and normalization
Topic 4: Debugging and Deploying10%- Deployment using bundles, CLI, and APIs
- Troubleshooting pipelines and errors
- CI/CD and DevOps practices
Topic 5: Data Sharing and Federation5%- Cross-workspace and cross-cloud access
- Unity Catalog data sharing
Topic 6: Ensuring Data Security and Compliance10%- Compliance standards implementation
- Data encryption and masking
- Access control and permissions
Topic 7: Data Ingestion & Acquisition7%- Schema inference and evolution
- Auto Loader and streaming ingestion
- Connecting to diverse data sources
Topic 8: Data Governance7%- Unity Catalog management
- Data lineage and metadata tracking
- Policy enforcement
Topic 9: Developing Code for Data Processing using Python and SQL22%- Data transformation and aggregation
- Batch and incremental processing logic
- Integration with Databricks APIs and tools
Topic 10: Cost & Performance Optimisation13%- Storage optimization (partitioning, Z-order, indexing)
- Cluster configuration and scaling
- Query optimization and caching

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