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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:

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

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