Databricks Databricks-Certified-Data-Engineer-Associate Exam Details & Actual Exam Questions

Exam Code/Number: Databricks-Certified-Data-Engineer-Associate
Exam Name/Title: Databricks Certified Data Engineer Associate Exam
Certification Provider: Databricks
Corresponding Certification: Databricks Certification

Exam Questions: 234
Updated On: Jul,17 2026
Certification Level: Associate

Databricks Certified Data Engineer Associate Exam Questions

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Databricks Databricks-Certified-Data-Engineer-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Engineer Associate Exam
Exam Number:	DE-A
Passing Score:	Approximately ~70% (not officially fixed/publicly guaranteed)
Exam Duration:	90 minutes
Real Exam Qty:	45 scored multiple-choice questions
Related Certifications:	Databricks Certified Data Engineer Professional
Available Languages:	English
Certificate Validity Period:	2 years
Exam Format:	Multiple-choice, Scenario-based questions, Proctored online or test center
Exam Price:	USD 200
Sample Questions:	Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions
Exam Way:	Online proctored exam or test center
Pre Condition:	No formal prerequisites required; recommended ~6 months hands-on experience with Databricks, Spark SQL, and PySpark.
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-associate

Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Objectives
Data Governance and Quality	- Unity Catalog basics - Data quality concepts and management - Data access control and governance
Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts
Productionizing Data Pipelines	- Pipeline deployment and operationalization - Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs
Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data
Data Processing and Transformations	- Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs)