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Databricks Certified Professional Data Engineer exam consists of multiple-choice questions and is conducted online. Databricks-Certified-Professional-Data-Engineer-KR exam is intended to measure the candidate's proficiency in various areas, such as Spark architecture, Spark programming, data processing, data analysis, and data modeling. Databricks-Certified-Professional-Data-Engineer-KR exam also tests the candidate's ability to optimize Spark performance and troubleshoot Spark applications. It is recommended that individuals who plan to take Databricks-Certified-Professional-Data-Engineer-KR exam have at least two years of hands-on experience in big data technologies and Apache Spark.
Reference: https://www.databricks.com/learn/certification/data-engineer-professional
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The Databricks Certified Professional Data Engineer Exam (Databricks-Certified-Professional-Data-Engineer Korean Version) certification verifies that the candidate has significant experience in implementing big data solutions that operate on the Databricks Delta Architecture. To earn the certification, a candidate must pass a 90-minute exam consisting of up to 50 multiple-choice and multiple-select questions. The Databricks-Certified-Professional-Data-Engineer certification is valid for two years from the date of passing the exam.
Databricks Databricks-Certified-Professional-Data-Engineer Korean Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Debugging and Deploying | 10% | - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors |
| Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Data Modelling | 6% | - Delta Lake table design - Schema design and management - Medallion Architecture implementation |
| Data Governance | 7% | - Policy enforcement - Data lineage and metadata tracking - Unity Catalog management |
| Developing Code for Data Processing using Python and SQL | 22% | - Integration with Databricks APIs and tools - Data transformation and aggregation - Batch and incremental processing logic |
| Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data |
| Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion |
| Monitoring and Alerting | 10% | - Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications |
| Ensuring Data Security and Compliance | 10% | - Access control and permissions - Compliance standards implementation - Data encryption and masking |
| Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching |
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