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Databricks Certified Professional Data Engineer exam is a rigorous certification exam that requires extensive knowledge and experience in data engineering. Candidates must have a deep understanding of data engineering concepts, such as data modeling, data warehousing, ETL, data governance, and data security. Additionally, they must have experience working with Databricks tools and technologies, such as Apache Spark, Delta Lake, and MLflow. Passing Databricks-Certified-Professional-Data-Engineer exam demonstrates that the candidate has the skills and knowledge needed to build and optimize data pipelines on the Databricks platform.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
Databricks Certified Professional Data Engineer exam is a comprehensive certification exam that assesses an individual's knowledge and skills in working with big data and cloud computing technologies. Databricks-Certified-Professional-Data-Engineer exam is designed for data professionals who are proficient in using Databricks Unified Analytics Platform for managing and analyzing large volumes of data. Databricks-Certified-Professional-Data-Engineer exam covers a broad range of topics such as data engineering, data transformation, data modeling, and machine learning.
Databricks Certified Professional Data Engineer exam covers a wide range of topics, such as data ingestion, transformation, storage, and processing. Databricks-Certified-Professional-Data-Engineer exam tests the candidates' ability to use Databricks tools and technologies to solve real-world problems and challenges. Candidates who pass Databricks-Certified-Professional-Data-Engineer exam demonstrate their proficiency in designing, building, and managing data pipelines with Databricks, which is a leading cloud-based platform for big data processing and analytics.
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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion | 15-20% | - Streaming ingestion
|
| Topic 2: Data Processing with Spark | 25-30% | - Python and SQL for data engineering
|
| Topic 3: Data Warehouse and Lakehouse Architecture | 15-20% | - Lakehouse architecture principles
|
| Topic 4: Delta Lake | 20-25% | - Delta Lake operations
|
| Topic 5: Pipeline Development and Orchestration | 10-15% | - Databricks workflows
|
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