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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) certification exam is designed for professionals who want to demonstrate their expertise in building and maintaining data pipelines on the Databricks platform. Databricks-Certified-Data-Engineer-Associate exam validates the skills and knowledge required to design, build, and maintain data pipelines on Databricks, and is a recognition of the candidate's ability to work with big data technologies and tools.
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The Databricks Databricks-Certified-Data-Engineer-Associate exam is a 90-minute online exam that consists of 60 multiple-choice questions. Databricks-Certified-Data-Engineer-Associate exam covers various topics such as data ingestion, data transformation, data modeling, data warehousing, and data analysis. Databricks-Certified-Data-Engineer-Associate exam is designed to validate the candidate's ability to work with Databricks in a production environment.
Reference: https://www.databricks.com/learn/certification/data-engineer-associate
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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Pipeline Architecture | 15-20% | - Understand ELT vs ETL patterns - Monitor and optimize pipeline performance - Implement incremental data processing - Design data pipelines for batch and streaming |
| Topic 2: Python for Data Engineering | 10-15% | - Use PySpark for data processing - Work with Spark APIs in Python - Implement user-defined functions (UDFs) |
| Topic 3: Apache Spark Data Processing Fundamentals | 20-25% | - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Use Spark SQL for data processing - Create and use Spark DataFrames |
| Topic 4: Lakehouse Platform Concepts | 10-15% | - Explain data governance and security concepts - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components |
| Topic 5: Delta Lake Fundamentals | 20-25% | - Write to and read from Delta tables - Create and manage Delta tables - Understand ACID transactions and time travel - Explain Delta Lake features and benefits |
| Topic 6: Spark SQL and DataFrames | 15-20% | - Handle null values and data quality - Join and union DataFrames - Write and execute Spark SQL queries - Aggregate and group data |
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