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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification exam that is designed for people who are interested in mastering the skills required for data engineering using the Databricks platform. Databricks Certified Data Engineer Associate Exam certification is globally recognized and is highly valued by employers since it proves that the certified individual has the knowledge and skills required to work with Databricks.
The Databricks Databricks-Certified-Data-Engineer-Associate exam is intended for data engineers, data architects, and developers who are responsible for designing, building, and maintaining data pipelines. Databricks Certified Data Engineer Associate Exam certification exam is comprised of 60 multiple-choice questions, and candidates have 90 minutes to complete the exam. Databricks-Certified-Data-Engineer-Associate exam measures candidates' knowledge and skills in various areas, including data ingestion, data transformation, and data processing.
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Databricks Certified Data Engineer Associate exam measures an individual's ability to design and implement data pipelines, optimize and tune big data solutions, and manage data workflows using Databricks. Candidates are expected to have a solid understanding of data processing techniques, data storage, and data management concepts. Databricks Certified Data Engineer Associate Exam certification program offers a comprehensive assessment of an individual's skills in the field of big data engineering and provides a valuable credential for those looking to advance their careers in this field.
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 |
|---|---|---|
| Apache Spark Data Processing Fundamentals | 20-25% | - Apply transformations and actions on DataFrames - Work with structured data types (arrays, maps, structs) - Use Spark SQL for data processing - Create and use Spark DataFrames |
| Delta Lake Fundamentals | 20-25% | - Create and manage Delta tables - Write to and read from Delta tables - Understand ACID transactions and time travel - Explain Delta Lake features and benefits |
| Python for Data Engineering | 10-15% | - Use PySpark for data processing - Implement user-defined functions (UDFs) - Work with Spark APIs in Python |
| Data Pipeline Architecture | 15-20% | - Design data pipelines for batch and streaming - Implement incremental data processing - Understand ELT vs ETL patterns - Monitor and optimize pipeline performance |
| Lakehouse Platform Concepts | 10-15% | - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components - Explain data governance and security concepts |
| Spark SQL and DataFrames | 15-20% | - Aggregate and group data - Handle null values and data quality - Join and union DataFrames - Write and execute Spark SQL queries |
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