The GAQM Databricks-Certified-Data-Engineer-Associate exam consists of 60 multiple-choice questions that cover a range of topics related to data engineering with Databricks. Databricks-Certified-Data-Engineer-Associate exam is designed to test the candidate's knowledge and skills in areas such as data ingestion, data transformation, data modeling, data integration, and data processing. Candidates who pass the exam will receive a certificate that validates their expertise in data engineering with Databricks and demonstrates their ability to work effectively with the platform. Databricks Certified Data Engineer Associate Exam certification is valuable for professionals working in data engineering roles, as it helps them to showcase their skills and expertise to potential employers and clients.
Reference: https://www.databricks.com/learn/certification/data-engineer-associate
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Databricks Certified Data Engineer Associate certification exam is a rigorous assessment of a candidate's knowledge and skills in various areas of data engineering. Databricks-Certified-Data-Engineer-Associate exam covers topics such as data ingestion, data transformation, data storage, data processing, and data analysis. Candidates must demonstrate proficiency in working with various Databricks tools, including Delta Lake, Spark SQL, and Apache Spark.
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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Delta Lake Fundamentals | 20-25% | - Explain Delta Lake features and benefits - Create and manage Delta tables - Write to and read from Delta tables - Understand ACID transactions and time travel |
| Topic 2: Data Pipeline Architecture | 15-20% | - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns - Monitor and optimize pipeline performance - Implement incremental data processing |
| Topic 3: Apache Spark Data Processing Fundamentals | 20-25% | - Apply transformations and actions on DataFrames - Use Spark SQL for data processing - Create and use Spark DataFrames - Work with structured data types (arrays, maps, structs) |
| 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: Spark SQL and DataFrames | 15-20% | - Aggregate and group data - Join and union DataFrames - Handle null values and data quality - Write and execute Spark SQL queries |
| Topic 6: Python for Data Engineering | 10-15% | - Work with Spark APIs in Python - Use PySpark for data processing - Implement user-defined functions (UDFs) |








