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Databricks Certified Professional Data Engineer exam measures a candidate's ability to design, build, and manage data pipelines using Databricks. It covers a wide range of topics, including data ingestion, transformation, storage, and analysis. Candidates must demonstrate their proficiency in using Databricks tools and techniques to solve real-world data engineering problems. Databricks Certified Professional Data Engineer Exam certification exam is ideal for data engineers who want to validate their skills and expertise in using Databricks to build and manage data pipelines.
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Databricks Certified Professional Data Engineer exam consists of a set of performance-based tasks that test the candidate's ability to apply their knowledge and skills to real-world scenarios. Databricks-Certified-Professional-Data-Engineer exam is conducted online and can be taken from anywhere in the world. Databricks-Certified-Professional-Data-Engineer exam is timed and candidates have to complete the tasks within the given time frame. Databricks-Certified-Professional-Data-Engineer exam is designed in such a way that it assesses the candidate's ability to work with Databricks Unified Analytics Platform and solve complex data engineering problems.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Security, Governance, Monitoring, and Optimization | - Implement Unity Catalog governance and access control - Cost optimization and performance tuning - Monitor and optimize Spark workloads |
| Topic 2: Data Ingestion and Transformation | - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines - Ingest data using Apache Spark and Databricks |
| Topic 3: Production Pipelines and Orchestration | - Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling |
| Topic 4: Data Modeling and Storage | - Delta Lake table design and optimization - Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures |
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