DATABRICKS-DE-PROFESSIONAL: Certified Data Engineer Professional

About the DATABRICKS-DE-PROFESSIONAL Exam

Master the Databricks Certified Data Engineer Professional exam (30 November 2025 guide) — building production data pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader, packaging and deploying with Databricks Asset Bundles and Git Folders, advanced Spark SQL and PySpark transformations, cost and performance optimization with deletion vectors and Liquid Clustering, data security with row filters and column masks, Unity Catalog governance, Delta Sharing and Lakehouse Federation, observability with system tables, and debugging and deployment.

The complete practice exam for the Databricks Certified Data Engineer Professional certification (current 30 November 2025 exam guide). Covers developing code for data processing in Python and SQL (scalable project structure for Databricks Asset Bundles, third-party library and dependency management, Pandas and Python UDFs, building and testing ETL with Lakeflow Spark Declarative Pipelines / Delta Live Tables, production batch and streaming pipelines with Auto Loader, automating Jobs via UI / REST API / CLI, streaming tables vs materialized views, APPLY CHANGES INTO for change data capture, Spark Structured Streaming vs declarative pipelines, and unit and integration testing with assertDataFrameEqual and assertSchemaEqual), data ingestion, transformation and quality (ingesting Delta, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from message buses and cloud storage, append-only batch and streaming pipelines, efficient Spark SQL and PySpark window functions, joins and aggregations, and quarantining bad data with pipeline expectations), cost and performance optimization (Unity Catalog managed tables, deletion vectors, Liquid Clustering, data skipping and file pruning, Change Data Feed, and reading the query profile to find join, shuffle and skipping bottlenecks), data security, compliance and governance (ACLs, row filters and column masks, anonymization and pseudonymization, PII masking pipelines, data purging for retention, metadata for discoverability, and the Unity Catalog permission inheritance model), monitoring, debugging and deployment (system tables, Query Profiler and Spark UI, REST API and CLI monitoring, Lakeflow event logs, SQL Alerts, repairing failed runs, and CI/CD with Databricks Asset Bundles and Git Folders), data sharing and federation (Delta Sharing D2D and open protocol D2O, recipients and shares, and Lakehouse Federation foreign catalogs), and data modeling (scalable Delta Lake models, Liquid Clustering over partitioning and ZORDER, and dimensional and slowly changing dimension designs) — every question a real-world scenario with full explanations.

Who Should Take This Exam?

The Databricks Certified Data Engineer Professional is designed for experienced data engineers who design, build, and operate production data pipelines on the Databricks Data Intelligence Platform. It validates advanced skills across pipeline development with Lakeflow Spark Declarative Pipelines and Auto Loader, performance and cost optimization, Unity Catalog security and governance, data sharing and federation, and CI/CD deployment with Databricks Asset Bundles. Databricks recommends hands-on experience performing the data engineering tasks in the exam guide; all code is in Python and SQL.

Prerequisites: None (advanced data engineering hands-on experience strongly recommended)

Typical study time: 6-10 weeks of focused study

Exam Quick Facts

Exam Domains & Weights

The DATABRICKS-DE-PROFESSIONAL exam covers 7 domains (consolidated from the 10 sections of the official 30 November 2025 exam guide). Focus your study time based on the weights below — higher-weighted domains have more exam questions.

💡 Study tip: Developing Code for Data Processing (26%) is the single largest area — get fluent with Lakeflow Spark Declarative Pipelines (the 2025 rebrand of Delta Live Tables): streaming tables vs materialized views, APPLY CHANGES INTO for CDC, expectations, plus Auto Loader, Databricks Asset Bundles, and testing with assertDataFrameEqual / assertSchemaEqual (remember ignoreNullable defaults to True). Cost and Performance Optimization (14%) rewards deep knowledge of Liquid Clustering (CLUSTER BY, and CLUSTER BY AUTO), deletion vectors, data skipping on the first 32 columns, OPTIMIZE FULL to recluster existing data, and reading the query profile. For Security and Governance (12%), drill the Unity Catalog privilege model — USE CATALOG and USE SCHEMA are separate traversal gates from SELECT — plus row filters and column masks. Data Sharing (10%) tests Delta Sharing D2D vs open protocol D2O and Lakehouse Federation, and Data Modeling (10%) covers Liquid Clustering over partitioning/ZORDER and SCD Type 1/2 designs.

Practice Exam — 250 Questions

Prepare for the DATABRICKS-DE-PROFESSIONAL with our 250-question practice exam covering all 7 exam domains. Every question is a real-world scenario with detailed explanations and maps to the official exam objectives.

What you get:

• ✅ Exam simulation mode with timer
• ✅ Spaced repetition for weak areas
• ✅ Detailed explanations for every question
• ✅ Progress tracking across domains
• ✅ 20 free questions — no account needed

Databricks Certification Path

Start with the Data Engineer Associate (or Machine Learning Associate), then advance to this Data Engineer Professional and the other Professional and specialty tracks (Machine Learning Professional, Generative AI Engineer).

Study Tips

1. Start with the heaviest domain — Developing Code for Data Processing is more than a quarter of the exam
2. Use our practice exam — try the 20 free questions first to gauge your readiness
3. Review explanations — don’t just check if you got it right; read why each answer is correct
4. Simulate exam conditions — use the timed exam mode to practice under pressure
5. Check the official page — official exam details always have the latest objectives