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The Databricks Certified Professional Data Engineer Exam certification exam covers a range of topics, including data ingestion, transformation, and storage, ETL processes, data modeling, and machine learning. Candidates are tested on their ability to use Databricks tools and technologies to solve real-world data engineering problems. Databricks-Certified-Professional-Data-Engineer exam also evaluates the candidate's understanding of best practices for data engineering, including security, scalability, and cost optimization. By passing the Databricks Certified Professional Data Engineer exam, candidates can demonstrate their proficiency in Databricks data engineering technologies and enhance their job prospects in the field.
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 respected credential within the data engineering industry. Databricks Certified Professional Data Engineer Exam certification is specifically designed for professionals who have a deep understanding of data engineering principles, practices, and technologies. With this certification, data engineers can demonstrate their expertise in designing and building data pipelines, managing data workflows, and implementing data analytics solutions using Databricks.
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing Code for Data Processing using Python and SQL | 22% | - Data transformation and aggregation - Integration with Databricks APIs and tools - Batch and incremental processing logic |
| Topic 2: Data Modelling | 6% | - Medallion Architecture implementation - Delta Lake table design - Schema design and management |
| Topic 3: Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) |
| Topic 4: Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Topic 5: Monitoring and Alerting | 10% | - Setting up alerts and notifications - Performance and health monitoring - Pipeline observability and logging |
| Topic 6: Data Transformation, Cleansing, and Quality | 10% | - Handling missing or inconsistent data - Standardization and normalization - Data validation and quality checks |
| 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% | - Auto Loader and streaming ingestion - Connecting to diverse data sources - Schema inference and evolution |
| Topic 9: Data Governance | 7% | - Unity Catalog management - Data lineage and metadata tracking - Policy enforcement |
| Topic 10: Ensuring Data Security and Compliance | 10% | - Data encryption and masking - Access control and permissions - Compliance standards implementation |
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