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Exam Code: Databricks-Certified-Professional-Data-Engineer
Exam Name: Databricks Certified Professional Data Engineer Exam
Updated: Aug 04, 2026
Q & A: 217 Questions and Answers
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The Databricks Databricks-Certified-Professional-Data-Engineer exam is a comprehensive test that requires the candidates to demonstrate their ability to design and implement data processing systems on Databricks. Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions and performance-based tasks that assess the candidates' ability to solve real-world data engineering problems using Databricks. Databricks-Certified-Professional-Data-Engineer exam is intended to be challenging, and candidates are expected to have a deep understanding of data engineering principles and best practices.
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Databricks is a cloud-based data analytics platform that provides solutions for data engineering, machine learning, and analytics. The Databricks-Certified-Professional-Data-Engineer (Databricks Certified Professional Data Engineer) Exam is a certification program designed to validate the skills and knowledge of data engineers working with the Databricks platform. Databricks-Certified-Professional-Data-Engineer exam is designed to assess the candidate's ability to design, implement, and manage data pipelines using Databricks.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformation, Cleansing, and Quality | 10% | - Handling missing or inconsistent data - Data validation and quality checks - Standardization and normalization |
| Topic 2: Data Governance | 7% | - Data lineage and metadata tracking - Unity Catalog management - Policy enforcement |
| Topic 3: Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices - Troubleshooting pipelines and errors |
| Topic 4: Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) |
| Topic 5: Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Topic 6: 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 7: Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion |
| Topic 8: Data Modelling | 6% | - Schema design and management - Delta Lake table design - Medallion Architecture implementation |
| Topic 9: Monitoring and Alerting | 10% | - Setting up alerts and notifications - Performance and health monitoring - Pipeline observability and logging |
| Topic 10: Ensuring Data Security and Compliance | 10% | - Access control and permissions - Compliance standards implementation - Data encryption and masking |
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