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Databricks Certified Professional Data Engineer certification exam is suitable for data engineers, data architects, and data scientists who are responsible for building and managing data pipelines and workflows. Databricks-Certified-Professional-Data-Engineer exam is designed to test the knowledge and skills required to design, implement, and manage data engineering workflows using Databricks. Candidates must have a solid understanding of data engineering concepts such as data modeling, data integration, data transformation, and data storage.

Reference: https://www.databricks.com/learn/certification/data-engineer-professional

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Databricks Certified Professional Data Engineer certification exam is a valuable credential for data engineers who work with Databricks. It demonstrates to potential employers and clients that the holder has the necessary skills and knowledge to work effectively with the platform. Additionally, the certification can enhance the holder's career prospects by providing opportunities for advancement and higher-paying positions.

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

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