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Databricks DE-ASSOC Guide: Data Engineer Associate

Databricks DE-ASSOC exam guide covering ingestion, pipelines, governance, and platform decisions.

This guide targets Databricks Certified Data Engineer Associate (DE-ASSOC), Databricks’ associate-level certification for notebook-based ETL, Delta tables, Lakeflow pipelines, production jobs, and Unity Catalog governance. As of April 15, 2026, Databricks’ live certification page and the current January 2026 exam guide both use the same 5-section public blueprint. This guide follows that live structure directly.

Databricks Data Intelligence Platform: Databricks workspace, compute, storage abstractions, governance, and managed data-engineering features working together as one operating environment.

Unity Catalog: Databricks governance layer for catalogs, schemas, tables, permissions, lineage, and sharing.

At a glance

DE-ASSOC does not reward raw Spark syntax recall by itself. Strong answers usually start by classifying the question into the right lane first: platform fit, development and ingestion, transformation logic, production operations, or governance. The trap is often not a silly answer. The trap is choosing a fix from the wrong layer of the Databricks workflow.

Exam fact Current official value
Scored questions 45 multiple-choice
Time limit 90 minutes
Registration fee USD 200
Delivery Online or test center
Recommended experience Roughly 6 months of hands-on Databricks work
Validity 2 years
Guide model 5 blueprint chapters -> 15 section lessons

The current exam guide PDF was published in January 2026 for the version live as of November 30, 2025. Databricks publishes the section objectives directly, so this guide stays rooted to the same five-part map rather than inventing a generic data-engineering curriculum.

Official chapter map

  • 1. Platform: platform value, workspace behavior, performance-aware defaults, and compute fit
  • 2. Ingestion: notebooks, Databricks Connect, Auto Loader, and debugging
  • 3. Processing: medallion design, Lakeflow pipelines, DDL and DML, PySpark DataFrames, and transformation performance
  • 4. Pipelines: Asset Bundles, workflows, repair and rerun, serverless jobs, and Spark UI triage
  • 5. Governance: managed versus external tables, Unity Catalog permissions, auditability, lineage, Delta Sharing, and federation

Use this exam guide in order

  • Start with the study plan if you want a practical 30 / 60 / 90 day route.
  • Work through the five official chapters in order, starting with Platform and Ingestion.
  • Use the cheat sheet after you understand the chapter logic well enough to separate near-miss answers.
  • Work through the sample questions to practice ingestion, transformation, operations, and governance prompts with full explanations.
  • Keep the glossary open when Lakeflow, Unity Catalog, Delta, and workflow terms start to blur together.
  • Use the FAQ when you want exam-fit, readiness, or last-week guidance.
  • Keep the resources page nearby while you work through the official Databricks guide and primary docs.

Coverage map against the current live exam guide

Section What to study first
1. Platform 1.1 Platform Defaults, 1.2 Compute Fit
2. Ingestion 2.1 Notebooks & Dev, 2.2 Auto Loader, 2.3 Debugging & Triage
3. Processing 3.1 Medallion, 3.2 Lakeflow, 3.3 SQL & DataFrames, 3.4 Transform Performance
4. Pipelines 4.1 Asset Bundles, 4.2 Workflows & Jobs, 4.3 Spark UI & Tuning
5. Governance 5.1 Unity Catalog, 5.2 Permissions & Lineage, 5.3 Sharing & Federation

Review flow

    flowchart LR
	  A["1. Platform and compute fit"] --> B["2. Development and ingestion workflow"]
	  B --> C["3. Processing and transformation logic"]
	  C --> D["4. Deployment, jobs, and runtime triage"]
	  D --> E["5. Governance, sharing, and federation"]
	  E --> F["Cheat sheet, glossary, and final review"]

What strong answers usually do

  • classify whether the stem is mainly about platform choice, data ingestion, pipeline logic, deployment behavior, or governance
  • distinguish interactive development workflow from scheduled production workflow
  • prefer managed, observable, repeatable pipeline behavior over manual notebook habits
  • treat Unity Catalog, lineage, auditability, and sharing as part of the platform design rather than post-processing chores

In this section

Revised on Monday, June 15, 2026
DA-ASSOC
DE-PRO