Databricks Certified Data Engineer Professional Practice Test

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.

• 1.Data Processing (~30%): Building robust batch and incremental ETL pipelines; deduplicating data; implementing Change Data Capture (CDC) using Delta Lake Change Data Feed (CDF) and Lakeflow Spark Declarative Pipelines; applying Structured Streaming with Auto Loader, windowing, and watermarking; performing Delta Lake operations including MERGE, OPTIMIZE, ZORDER, and VACUUM; designing SCD Type 1 and Type 2 patterns.
• 2.Data Modeling and Medallion Architecture (~20%): Designing and implementing the Medallion Architecture (Bronze, Silver, Gold layers); applying lakehouse data modeling principles; managing schemas and schema evolution; using Unity Catalog for multi-workspace data organization and access control.
• 3.Workflow Orchestration (~20%): Configuring Databricks Workflows (Jobs and Tasks) for complex pipeline dependencies; implementing orchestration patterns including fan-out, funnel, and sequential execution; using dbutils for file and secret management; integrating Databricks Repos and version control; deploying and managing jobs via the Databricks CLI and REST API.
• 4.Security and Governance (~15%): Implementing fine-grained access control with Unity Catalog including row-level and column-level security; configuring data lineage tracking and audit logging; applying Delta Sharing for secure cross-platform data exchange; managing secrets and credentials; understanding Lakehouse Federation for querying external data sources.
• 5.Performance Optimization and Monitoring (~10%): Tuning Spark workloads for throughput and cost efficiency; applying Delta Lake performance features (file compaction, Z-ordering, liquid clustering); monitoring streaming query health using StreamingQueryListener; implementing observability best practices for production pipelines.
• 6.DevOps and CI/CD (~5%): Implementing CI/CD workflows for Databricks using Databricks Asset Bundles (DAB); automating deployment with the Databricks CLI and REST API; writing and running tests using frameworks like pytest; managing infrastructure-as-code for Databricks resources.

• Download and study the official Databricks Certified Data Engineer Professional Exam Guide PDF from the Databricks certification page — it defines exact domains, topic areas, and sample questions. Verify you have the most current version (updated March 2025) a few weeks before your exam date.
• Complete the 'Advanced Data Engineering with Databricks' course on Databricks Academy, which is specifically designed for this professional-level certification. Supplement with self-paced courses on Streaming and Lakeflow Spark Declarative Pipelines, Databricks Data Privacy, Performance Optimization, and Automated Deployment with Databricks Asset Bundles.
• Practice hands-on in a Databricks workspace (Community Edition or trial) — focus specifically on Delta Lake MERGE operations, CDC pipelines using Change Data Feed, Auto Loader ingestion patterns, and configuring multi-task Workflows. Scenario-based questions heavily test applied knowledge over theory.
• Study Unity Catalog in depth, including workspace-level vs. metastore-level permissions, row/column-level security, data lineage, audit logging, and Delta Sharing. The 2025 exam update significantly expanded Unity Catalog coverage.
• Take the official Databricks practice exams available through the certification portal to calibrate your readiness. Aim to score consistently above 80% on practice tests before sitting the exam, given that the official passing threshold is 70% but production-grade scenario questions demand deep understanding.
• Focus on streaming architecture patterns — understand exactly when to use Structured Streaming vs. Lakeflow Spark Declarative Pipelines, how to handle late data with watermarks, how to configure stateful aggregations, and how to monitor streaming jobs using StreamingQueryListener and the Workflows UI.
• Review Databricks Asset Bundles (DAB) and the Databricks CLI for deployment automation, as the 2025 exam update added increased emphasis on DevOps, CI/CD, and programmatic deployment of Databricks resources.

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.