Databricks Certified Data Engineer Professional Practice Tests

The current Databricks Certified Data Engineer Professional exam guide splits the exam into ten sections, and the weighting leans heavily toward hands-on coding. Developing code for data processing using Python and SQL is the largest section at 22 percent, followed by cost and performance optimization at 13 percent. A cluster of sections sit at 10 percent each: data transformation, cleansing, and quality; monitoring and alerting; ensuring data security and compliance; and debugging and deploying. The remaining weight is spread across data ingestion and acquisition (7 percent), data governance (7 percent), data modelling (6 percent), and data sharing and federation (5 percent). Our 628-question bank is built to match that split, so you spend the most reps on data-processing code and performance tuning rather than the lighter governance and sharing topics.

The exam runs 59 scored multiple-choice questions in 120 minutes, delivered online through a remote proctor. A few unscored items may be mixed in for statistical calibration; they are not labeled and do not affect your result. Questions are scenario-based rather than recall-based, so expect Spark UI screenshots, streaming query output, and PySpark or SQL snippets where you diagnose what is wrong or pick the correct fix. Databricks does not publish an official passing score, but candidates widely report a threshold around 70 percent, which works out to roughly 42 of 59 questions. Pacing is about two minutes per question, so the live-troubleshooting items are where time gets tight.

There are no formal prerequisites, but Databricks recommends holding the Data Engineer Associate credential and at least one year of hands-on experience building production pipelines on the platform. The exam costs $200 USD plus any local tax, and the certification is valid for two years; to renew you retake the current version of the exam, which changes as the platform evolves. Passing the exam is the only requirement for the credential, with no separate application or continuing-education filing. Start with the 30 free questions, then work through the full 628-question bank until your accuracy holds steady across all ten domains.

The Databricks Certified Data Engineer Professional certification validates advanced proficiency in building, optimizing, and maintaining production-grade data engineering solutions on the Databricks Data Intelligence Platform. Successful candidates demonstrate deep expertise across core platform capabilities including Delta Lake, Unity Catalog, Auto Loader, Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables), Databricks Compute (including serverless), Lakeflow Jobs, and the Medallion Architecture. The exam was updated in 2025 to reflect a Data Intelligence Platform framing, with expanded coverage of AI-driven features, Delta Sharing, Lakehouse Federation, Databricks Asset Bundles (DAB), and enhanced Unity Catalog governance.

This certification assesses the ability to design secure, reliable, and cost-effective ETL pipelines; process complex data from diverse sources using Python and SQL; implement Change Data Capture (CDC), SCD1, and SCD2 patterns; and apply best practices in schema management, observability, performance optimization, and data governance. Candidates are also evaluated on streaming workloads using Structured Streaming, workflow orchestration via Databricks Workflows, and deployment automation using the Databricks CLI, REST API, and Asset Bundles.

This certification is designed for experienced data engineering professionals with at least one year of hands-on experience building and operating production data pipelines on Databricks. Ideal candidates include Senior Data Engineers, Lead Analytics Engineers, Data Architects, and Big Data professionals who work daily with Apache Spark, Delta Lake, and the Databricks Lakehouse Platform. Those who have already obtained the Databricks Certified Data Engineer Associate credential are especially well-positioned, as the Professional exam builds substantially on that foundational knowledge.

Professionals transitioning from traditional ETL development, data scientists who regularly build and maintain pipelines, and Solutions Architects seeking to validate deep platform expertise are also strong candidates. Code examples on the exam are primarily in Python and SQL, so comfort with PySpark and Spark SQL is essential.

There are no formal prerequisite certifications required, but Databricks strongly recommends holding or demonstrating mastery of the skills covered by the Databricks Certified Data Engineer Associate certification before attempting the Professional exam. Candidates should have at least one year of hands-on experience performing the data engineering tasks outlined in the official exam guide.

Recommended knowledge areas include proficiency with Apache Spark (PySpark and Spark SQL), Delta Lake operations (MERGE, OPTIMIZE, ZORDER, VACUUM, Change Data Feed), Structured Streaming concepts (Auto Loader, windowing, watermarking), Unity Catalog for data governance, Databricks Workflows for job orchestration, and familiarity with DevOps practices including version control and CI/CD pipelines. Candidates should be comfortable working in the Databricks Workspace, using the Databricks CLI and REST API, and applying the Medallion Architecture (Bronze, Silver, Gold layers) in real-world pipeline design.

The Databricks Certified Data Engineer Professional exam consists of approximately 60 scored multiple-choice questions, with some candidates reporting up to 65 questions. The exam duration is 120 minutes (2 hours). As with other Databricks exams, the form may include a small number of unscored survey items used to gather statistical data for future exam development; these are not identified and do not affect the final score, and additional time is factored in to account for them.

The exam is delivered online via a remote proctoring platform and costs $200 USD (plus applicable taxes). The passing score is 70%. Questions are scenario-based and require applied knowledge rather than rote memorization, frequently presenting realistic production engineering challenges in PySpark and SQL. Recertification is required every two years by retaking the current version of the exam.

The Databricks Certified Data Engineer Professional credential is recognized as an advanced-tier validation of Lakehouse Platform expertise, positioning holders for senior roles such as Senior Data Engineer, Lead Analytics Engineer, Data Architect, and Solutions Architect. Databricks is used by more than 7,000 organizations globally, including approximately 40% of Fortune 500 companies, creating sustained demand for certified professionals. Certified data engineers in the US typically earn between $115,000 and $150,000 annually, with top earners exceeding $160,000 depending on experience, location, and industry. Glassdoor data places the average at approximately $131,000, with a range extending to $170,000 at the 75th percentile.

Compared to the Associate-level certification, the Professional credential signals the ability to architect and operate enterprise-grade solutions — not just implement them — which substantially increases leverage in salary negotiations and job applications. The certification also serves as a differentiator against candidates holding generalist cloud data engineering credentials (e.g., AWS, Azure, GCP data engineer certs), as Databricks expertise is platform-specific and increasingly in demand as organizations adopt the Lakehouse architecture for unified analytics and AI workloads. Recertification every two years ensures holders stay current with the rapidly evolving platform.