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Updated: Aug 23, 2026
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Databricks Certified Professional Data Engineer exam is designed for data professionals who want to validate their skills in building and optimizing data pipelines on the Databricks platform. Databricks-Certified-Professional-Data-Engineer exam is intended to demonstrate a comprehensive understanding of data engineering principles, best practices, and practical skills required for designing, building, and maintaining robust, scalable, and efficient data pipelines using Databricks. Databricks Certified Professional Data Engineer Exam certification exam is recognized globally and provides a competitive edge in the job market.
Databricks Certified Professional Data Engineer exam is a hands-on exam that requires the candidate to complete a set of tasks using Databricks. Databricks-Certified-Professional-Data-Engineer exam evaluates the candidate's ability to design and implement data pipelines, work with data sources and sinks, and perform transformations using Databricks. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's ability to optimize and tune data pipelines for performance and reliability.
Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:
| Certification Vendor: | Databricks |
| Exam Name: | Databricks Certified Professional Data Engineer Exam |
| Exam Number: | Databricks-Certified-Professional-Data-Engineer |
| Exam Format: | Multiple Choice |
| Related Certifications: | Databricks Certified Associate Data Engineer |
| Real Exam Qty: | 59 |
| Passing Score: | 70% |
| Available Languages: | English, Japanese, Portuguese (Brazil), Korean |
| Exam Price: | USD 200 |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Databricks Data Engineer Professional Training |
| Exam Registration: | Databricks Official Certification Registration |
| Sample Questions: | Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions |
| Exam Way: | Online proctored or onsite test center |
| Pre Condition: | No mandatory prerequisites; 1+ year hands-on experience and related training highly recommended |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-professional |
Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is designed for professionals who want to demonstrate their expertise in using Databricks to manage big data and create data pipelines. Databricks Certified Professional Data Engineer Exam certification exam is ideal for data engineers, data architects, data scientists, and other professionals who work with big data and want to validate their skills in using Databricks to build data pipelines.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing Code for Data Processing using Python and SQL | 22% | - Batch and incremental processing logic - Integration with Databricks APIs and tools - Data transformation and aggregation |
| Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Data Governance | 7% | - Data lineage and metadata tracking - Policy enforcement - Unity Catalog management |
| Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Handling missing or inconsistent data - Standardization and normalization |
| Monitoring and Alerting | 10% | - Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring |
| Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Query optimization and caching - Storage optimization (partitioning, Z-order, indexing) |
| Ensuring Data Security and Compliance | 10% | - Data encryption and masking - Compliance standards implementation - Access control and permissions |
| Debugging and Deploying | 10% | - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors |
| Data Modelling | 6% | - Medallion Architecture implementation - Schema design and management - Delta Lake table design |
| Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion |
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