Databricks DE-PRO FAQ: Exam Format, Topics, and Prep

What is DE-PRO?

DE-PRO is the Databricks Certified Data Engineer Professional exam. It tests advanced production data-engineering work on Databricks: code organization, pipeline design, observability, performance, governance, data sharing, CI/CD, and debugging.

What is the current live exam format?

As of April 13, 2026, the live Databricks certification page lists:

• 59 scored questions
• 120 minutes
• $200
• no prerequisite certification
• 2 years validity
• English, Japanese, Portuguese BR, and Korean

The current Databricks exam guide PDF says the version it describes is live as of September 30, 2025. The public certification page says delivery is online or test center, while the PDF says online proctored. Re-check the live Databricks page before booking.

How is DE-PRO different from DE-ASSOC?

DE-PRO is less about “can you build the first version?” and more about “can you operate, diagnose, secure, and promote it safely?”

Who is this exam really for?

This exam fits candidates who can already do most of these without bluffing:

• explain why a checkpoint, watermark, row filter, or repair run changes system behavior
• separate pipeline logic from orchestration and deployment
• read the right signal source before changing the design
• choose between sharing, federation, masking, or inheritance with clear boundaries
• defend a performance choice in terms of layout, pruning, joins, and cost

What topics matter most?

The live Databricks certification page weights the 10 domains as:

• Developing Code for Data Processing using Python and SQL: 22%
• Data Ingestion & Acquisition: 7%
• Data Transformation, Cleansing, and Quality: 10%
• Data Sharing and Federation: 5%
• Monitoring and Alerting: 10%
• Cost & Performance Optimisation: 13%
• Ensuring Data Security and Compliance: 10%
• Data Governance: 7%
• Debugging and Deploying: 10%
• Data Modelling: 6%

The highest-pressure misses usually happen in code and deployment structure, monitoring, performance, governance, and security because those domains force you to separate operational layers instead of memorizing one feature.

What are common weak spots?

• using older DLT framing without mapping it to the current Lakeflow terminology
• scaling compute before reading query profile, event logs, system tables, or Spark UI evidence
• mixing Delta Sharing, Lakehouse Federation, and Unity Catalog inheritance into one permissions concept
• treating repair and rerun questions like notebook debugging instead of production run control
• ignoring data-model or layout consequences when answering performance questions

What hands-on baseline is actually useful?

Before you rely heavily on timed sets, you should be able to explain or demonstrate:

• one package or bundle structure that promotes across environments
• one ingestion path where you can defend batch, append-only Delta, or streaming behavior
• one monitoring path where you know when to use event logs, system tables, query profile, or Spark UI
• one security and governance path involving filters, masks, ACLs, inheritance, or sharing
• one performance case where you can separate layout, pruning, shuffle, and join issues from pure compute pressure

How should I review misses?

How do I know I am close to ready?

You are close when:

• your misses narrow to a few repeat domains rather than the whole blueprint
• you can explain the production consequence of the winning answer, not just the feature name
• you stop choosing fixes that are hard to rerun or hard to audit
• you can tell when the question is really about boundary design rather than raw code

Which official source wins if something disagrees?

Use the current Databricks certification page for booking details and the current DE-PRO exam guide PDF for detailed scope. Both should be re-checked near your exam date because Databricks updates these materials over time.