Databricks Certified Data Engineer Associate: Exam Guide

The Databricks Certified Data Engineer Associate exam validates an individual’s ability to build, maintain, and operate data pipelines on Databricks Data Intelligence Platform. It is the entry-level credential in the Databricks data engineering track, sitting below the Professional-level certification, and is aimed at engineers, ETL developers, and analytics engineers who work with Spark, Delta Lake, and Databricks-native orchestration tools.

Databricks revised the exam blueprint in May 2026.

Databricks Exam Format

• Format: 45 multiple-choice questions
• Duration: 90 minutes
• Cost: USD $200 plus applicable local taxes
• Delivery: Online proctored or at an authorized testing center
• Validity: 2 years, after which recertification requires retaking the current exam version
• Prerequisites: None formally required; Databricks recommends roughly six months of hands-on platform experience
• Code samples appear in SQL where possible and in Python (PySpark) elsewhere

Official Exam Domains (May 2026)

The exam covers seven domains. The table below uses the exact domain names, numbers, and weightings from the official Databricks exam guide.

Skills You Should Be Comfortable With

• PySpark DataFrames and transformations
• Spark SQL for querying and transforming data
• Delta Lake: MERGE, time travel (VERSION AS OF), OPTIMIZE, VACUUM, ACID guarantees
• Auto Loader and COPY INTO for incremental ingestion
• Lakeflow Declarative Pipelines (formerly Delta Live Tables)
• Lakeflow Jobs (formerly Databricks Workflows) and Declarative Automation Bundles:
• Unity Catalog: metastores, table/column-level permissions, row filters, lineage
• Medallion Architecture (Bronze → Silver → Gold)
• Basic CI/CD concepts as applied to Databricks deployments
• Cluster types and compute selection: all-purpose vs. job clusters, serverless vs. classic
• Data Ingestion Patterns (including Spark Streaming)

Recommended Experience

Databricks designs this exam to test applied judgment rather than memorization. Most questions are scenario-based: a situation is described with explicit constraints, and the candidate must select the most appropriate solution rather than recall a fact.

Recommended background includes:

• Roughly six months of hands-on Databricks experience
• Working SQL knowledge
• Basic Python and PySpark familiarity
• Experience building at least one ETL pipeline on the platform, even in a sandbox or Community Edition environment

4-Week Study Plan

Week 1: Platform Foundations

• Databricks workspace navigation and architecture
• Cluster types and compute selection
• Delta Lake fundamentals: transaction log, ACID guarantees
• Spark SQL and PySpark basics

Week 2: Ingestion and Transformation

• Auto Loader configuration and schema evolution
• COPY INTO and streaming ingestion patterns
• Delta Lake DML: MERGE, UPDATE, DELETE, time travel
• Medallion Architecture and transformation design

Week 3: Productionizing and Governance

• Lakeflow Jobs and Lakeflow Declarative Pipelines
• Declarative Automation Bundles: for deployment
• Unity Catalog: metastore setup, table and column-level grants
• Row-level security and lineage tracking

Week 4: Consolidation

• Basic CI/CD concepts and deployment workflows
• Cluster troubleshooting and Spark UI analysis
• 3–5 full-length practice exams under timed conditions
• Review every incorrect answer against current Databricks documentation, not older third-party guides

Best Study Resources

• Official Databricks Academy courses (primary source)
• The official exam guide, downloaded directly from credentials.databricks.com
• Current Databricks documentation: Delta Lake, Unity Catalog, Auto Loader, Lakeflow Jobs, Lakeflow Declarative Pipelines
• Hands-on practice building ingestion pipelines, Delta tables, scheduled jobs, and Unity Catalog permission structures
• Practice exams to identify weak domains, cross-checked against the current blueprint rather than older question banks

Is It Worth It?

For engineers already working with Databricks, or moving into lakehouse-based data engineering roles, the certification remains a recognized signal of practical platform competency. It demonstrates working knowledge of Apache Spark, Delta Lake, and production pipeline patterns, and is commonly listed as a preferred qualification for Data Engineer roles across Azure Databricks, AWS Databricks, and Databricks on Google Cloud. Because the credential is cloud-agnostic, it also transfers across employers regardless of which cloud platform they run Databricks on.

Few Words from Our Senior Data Engineer Cerfitied in 2026

On study strategy:

If you already work with Databricks day-to-day, there’s no need to go through every course from scratch. Focus on practice exams to understand the question format, then go back to the official documentation specifically in the areas where you score poorly. The exam guide lists every topic area – use it as a checklist rather than a reading list.

On practice tests:

The practice exam questions are a very similar style to the real thing, but don’t expect them to repeat verbatim. The value is in learning the question format and identifying your weak spots, not in memorizing specific answers.

Expect a lot of code-reading questions – given a PySpark or SQL snippet, select the correct one for a given ingestion scenario. Asset Bundles had significant coverage: repo structure, databricks.yml file structure, and deployment constraints. Compute type selection came up repeatedly.

On Medallion Architecture – the tricky ones:

Some questions are straightforward, but a few are genuinely ambiguous due to a terminology issue: Bronze Layer and Raw Layer are used interchangeably in Databricks’ own documentation. When an answer option says “raw layer” and another says “bronze layer,” it’s not always clear which one they intend. I encountered three or four questions of this type and could not determine with confidence which was considered correct. Be aware of this going in.