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Databricks DE-PRO Guide: Data Engineer Professional

Databricks DE-PRO exam guide covering orchestration, optimization, governance, and production readiness decisions.

This guide targets Databricks Certified Data Engineer Professional (DE-PRO), Databricks’ professional-level data-engineering certification for candidates who already know the platform and now need to prove production judgment. As of April 13, 2026, the live Databricks certification page and the current September 2025 exam guide both use a 10-domain blueprint. This guide follows that live structure directly.

Lakeflow Declarative Pipelines: Databricks’ managed declarative pipeline layer for batch and streaming ETL, previously framed in older material as DLT.

System tables: Databricks account and workspace telemetry tables used for cost, audit, and workload observability.

Databricks Asset Bundles: Databricks packaging and deployment structure for repeatable multi-environment resource promotion.

At a glance

Exam fact Current official signal
Scored questions 59
Time limit 120 minutes
Registration fee $200
Languages on live certification page English, Japanese, Portuguese BR, Korean
Recommended experience hands-on experience performing the advanced data-engineering tasks in the guide; the PDF strongly recommends about 1 year
Validity 2 years
Code note code examples are primarily in Python and SQL
Guide model 10 blueprint chapters -> 18 section lessons

The live Databricks sources are aligned on the section weights and core scope, but not every delivery detail is phrased the same way. As of April 13, 2026, the live certification page says online or test center, while the September 2025 exam guide says online proctored. Treat the live certification page as the final booking check and the current PDF as the deeper scope document.

DE-PRO is not a notebook-speed exam. It is mostly a production trade-off exam. Strong answers usually begin by classifying the failing layer first: code and packaging, ingestion design, data transformation, sharing or governance, monitoring, performance, security, deployment, or table-model design. The trap is often not a silly answer. The trap is mixing three operational layers together and choosing a fix that only works once.

How to use this guide

  1. Start with the study plan if you need a weighted route through the 10 domains.
  2. Work the chapters in order, because code structure, ingestion design, and transformation logic shape the later monitoring, deployment, and governance questions.
  3. Use the cheat sheet after the lessons, not before them, so the quick pickers reinforce production judgment instead of replacing it.
  4. Work through the sample questions to practice streaming recovery, deployment, monitoring, performance, and governance prompts with full explanations.
  5. Use the faq for current exam facts, DE-ASSOC vs DE-PRO expectations, and the current delivery wording mismatch across Databricks sources.
  6. Use the resources page to re-check the current certification page, exam guide PDF, and Databricks docs near your exam date.
  7. Use the glossary only when Lakeflow, Delta, Unity Catalog, system-table, sharing, or deployment terms start to blur together.

Blueprint-aligned chapter map

The live Databricks certification page publishes all 10 DE-PRO domain weights. This guide follows that map directly.

Exam domain Weight Chapter Start here
Developing Code for Data Processing using Python and SQL 22% 1. Code 1.1 Python Structure & Tests, 1.2 Lakeflow & Jobs
Data Ingestion & Acquisition 7% 2. Ingestion 2.1 Auto Loader & Sources, 2.2 Append-Only & Streaming
Data Transformation, Cleansing, and Quality 10% 3. Transformation 3.1 Joins, Windows & Transforms, 3.2 Quarantine & Expectations
Data Sharing and Federation 5% 4. Sharing 4.1 Delta Sharing & Federation
Monitoring and Alerting 10% 5. Monitoring 5.1 System Tables & Event Logs, 5.2 Alerts & Notifications
Cost & Performance Optimisation 13% 6. Performance 6.1 Managed Tables & Clustering, 6.2 Shuffle, Joins & CDF
Ensuring Data Security and Compliance 10% 7. Security 7.1 ACLs, Masks & Least Privilege, 7.2 PII & Retention
Data Governance 7% 8. Governance 8.1 Metadata & UC Inheritance
Debugging and Deploying 10% 9. Debugging 9.1 Spark UI & Job Repair, 9.2 Asset Bundles & CI/CD
Data Modelling 6% 10. Modelling 10.1 Delta Design & Partitioning, 10.2 Dimensional Modeling & Serving 
    flowchart LR
	  A["1. Code and packaging discipline"] --> B["2. Ingestion and transformation choices"]
	  B --> C["3. Monitoring and performance evidence"]
	  C --> D["4. Security, governance, and sharing"]
	  D --> E["5. Debugging, deployment, and data modeling"]
	  E --> F["Cheat sheet, glossary, FAQ, and live Databricks checks"]

What strong answers usually do

  • preserve repeatability before chasing one-off speed
  • separate pipeline logic, orchestration, observability, security, and modeling concerns instead of fixing everything in one layer
  • prefer observable, replay-safe, low-blast-radius designs over notebook-only shortcuts
  • use the smallest useful operational signal first: event log, system table, query profile, Spark UI, or job state

Where candidates usually lose points

Failure pattern Better instinct
using old DLT habits without mapping them to the current Lakeflow framing translate older wording into Lakeflow Declarative Pipelines, Lakeflow Jobs, and current docs terms
scaling compute before reading profile, shuffle, layout, or pruning evidence inspect the bottleneck before resizing
treating security, governance, and sharing as one broad permissions topic separate ACLs, row filters, column masks, sharing protocol, and inheritance model
choosing manual notebook repair instead of a repeatable deployment or repair path prefer job repair, parameter override, bundles, and auditable promotion
picking an answer that works once but is hard to rerun professional-level questions usually reward low-blast-radius operations

In this section

Revised on Monday, June 15, 2026
DE-ASSOC
GENAI-ASSOC