The Databricks Certified Data Engineer Associate is a 45-question, 90-minute exam covering the Databricks Data Intelligence Platform, ETL with Spark SQL and PySpark, Delta Lake, Lakeflow Declarative Pipelines, and Unity Catalog. It costs $200 USD, is pass/fail (no published score threshold), and is valid for 2 years. No prerequisites, but 6+ months of hands-on Databricks experience is recommended.
- Understand the Databricks DE Associate exam format, five domains, and their weights
- Evaluate whether the Databricks certification fits your career stage and tech stack
- Follow a structured 4–6 week study plan with official Databricks Academy resources
- Compare Databricks DE Associate vs AWS DEA-C01 vs Microsoft DP-700
- Add the certification to your resume and LinkedIn effectively
Quick Answers
Is the Databricks Data Engineer Associate certification worth it?
Yes, if you work with Databricks or Spark. Databricks adoption has surged in modern data teams, and the certification validates skills in Delta Lake, Lakeflow, and Unity Catalog — concepts that appear directly in job postings for lakehouse-oriented roles.
How hard is the Databricks Data Engineer Associate exam?
Moderate difficulty. The exam assumes 6+ months of hands-on Databricks experience. If you use Spark SQL and Delta Lake daily, expect 4–6 weeks of study. If you're new to Databricks, plan for 8–10 weeks with heavy lab work.
What is the passing score for the Databricks DE Associate?
Databricks does not publish a specific passing score. The exam is scored as pass/fail. Community reports suggest approximately 70% correct answers are needed, but Databricks has not officially confirmed this.
Does the Databricks certification require prerequisites?
No formal prerequisites. Databricks recommends 6+ months of hands-on experience with the platform. Code on the exam is provided in SQL when possible; otherwise in Python.
Databricks has grown from a niche Spark vendor into the platform behind some of the largest data lakehouses in the world. Companies like Shell, Comcast, Columbia, and HSBC run production pipelines on Databricks. As the platform has expanded, so has demand for engineers who can prove they know it — and the Databricks Certified Data Engineer Associate is the credential that does exactly that.
Unlike AWS or Azure certifications that test broad cloud knowledge, the Databricks exam is laser-focused: Delta Lake, Lakeflow (formerly Delta Live Tables), Unity Catalog, Spark SQL/PySpark, and Databricks Workflows. If your daily work involves any of these, this certification maps directly to your job.
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Learn how Careery can help youWhat Is the Databricks Certified Data Engineer Associate?
- Databricks Certified Data Engineer Associate
A vendor-specific certification that validates an individual's ability to use the Databricks Data Intelligence Platform for introductory data engineering tasks — including ETL with Spark SQL and PySpark, Delta Lake table management, Lakeflow Declarative Pipelines, Databricks Workflows, and Unity Catalog governance.
The exam covers five domains:
- Databricks Intelligence Platform (10%) — platform architecture, workspace features, query optimization
- Development and Ingestion (30%) — Auto Loader, Databricks Connect, data ingestion patterns, debugging
- Data Processing & Transformations (31%) — Medallion Architecture, Delta Lake, Spark SQL, PySpark, Lakeflow Declarative Pipelines, DDL/DML
- Productionizing Data Pipelines (18%) — Databricks Asset Bundles, Workflows, scheduling, failure recovery, serverless compute
- Data Governance & Quality (11%) — Unity Catalog hierarchy, table types, permissions, external locations
The Databricks DE Associate is the only major data engineering certification focused on the lakehouse paradigm. It tests Delta Lake, Lakeflow, and Unity Catalog — the specific tools that define Databricks-based data engineering.
Is the Databricks Certification Worth It in 2026?
When It Helps Most
- Engineers at Databricks shops: If your team uses Databricks, the certification proves you understand the platform's architecture, not just how to write Spark queries. It signals depth to hiring managers who know the platform.
- Data engineers moving into lakehouse roles: The lakehouse pattern (Delta Lake + Unity Catalog) is becoming the dominant architecture for modern data teams. Certification shows you understand this paradigm, not just legacy warehouse patterns.
- Career changers with Spark experience: If you know Spark but haven't worked with Databricks specifically, the certification closes that gap and gives you a credential to back it up.
- Consultants and contractors: Databricks partner organizations often require certified engineers on client engagements. Some consulting tiers depend on the number of certified practitioners on staff.
When It May Not Be the Priority
- Teams that don't use Databricks or Spark: If your tech stack is entirely AWS Glue + Redshift or Microsoft Fabric, a cloud-native cert (DEA-C01 or DP-700) is more directly useful.
- Senior engineers already deep in Databricks: If you've been building production Databricks pipelines for 2+ years, your track record may speak louder than a certificate. Consider pairing a cloud cert (AWS or Azure) for maximum coverage.
For the full picture of how certification affects data engineer compensation — including experience breakdowns and location adjustments — see our Data Engineer Salary Guide.
The Databricks certification has the highest ROI for engineers already working with Spark or moving into lakehouse-architecture roles. If your target companies don't use Databricks, start with a cloud-platform cert instead.
Exam Overview: Format, Scoring, and Prerequisites
Code Language on the Exam
Databricks provides exam code in SQL when possible. Where SQL cannot express the concept (e.g., Auto Loader configuration, PySpark DataFrame operations), code is in Python. You do not need to know Scala for this exam.
Unscored Content
The exam may include unscored pilot questions for statistical evaluation. These are not identified, and additional time is built in to account for them. Treat every question as if it counts.
45 questions, 90 minutes, pass/fail, $200. Code is in SQL or Python — no Scala required. The 2-year validity means you recertify by retaking the exam, not via an online renewal assessment.
What's on the Exam: Domain Deep-Dive
Domain 1: Databricks Intelligence Platform (10%)
The smallest domain — but don't skip it entirely. Covers:
- Platform value proposition and architecture
- Workspace features and navigation
- Query performance optimization strategies
This domain tests whether you understand what Databricks is at an architectural level, not just how to write queries in it.
Domain 2: Development and Ingestion (30%)
The second-largest domain. Key topics:
- Auto Loader — incremental file ingestion from cloud storage, schema evolution handling, configuration syntax
- Databricks Connect — connecting external IDEs to Databricks clusters
- Ingestion patterns for batch and streaming data
- Debugging tools and techniques in notebooks
Auto Loader questions are among the most frequently tested. Know the difference between cloudFiles format options, schema inference behavior, and how Auto Loader handles schema evolution. Practice writing readStream code with Auto Loader in a notebook.
Domain 3: Data Processing & Transformations (31%)
The largest domain — nearly one-third of the exam. Covers:
- Medallion Architecture — Bronze (raw), Silver (cleaned), Gold (business-level) table patterns and when to use each
- Delta Lake — ACID transactions, time travel, VACUUM, OPTIMIZE, Z-ORDER, table properties
- Lakeflow Declarative Pipelines (formerly Delta Live Tables / DLT) — pipeline definitions, expectations (data quality rules), materialized views vs. streaming tables
- Spark SQL and PySpark — DDL/DML operations, complex aggregations, window functions, UDFs
- Cluster configuration and compute optimization
This domain alone is nearly a third of the exam. If you can only study one area deeply, make it this one. Know Delta Lake operations (MERGE INTO, time travel, OPTIMIZE) and Lakeflow pipeline syntax cold.
Domain 4: Productionizing Data Pipelines (18%)
- Databricks Asset Bundles (DAB) — packaging and deploying jobs, pipelines, and configurations as code
- Databricks Workflows — creating, scheduling, and monitoring multi-task jobs
- Failure recovery and retry strategies
- Serverless compute — when and why to use it
- CI/CD patterns for data engineering on Databricks
Domain 5: Data Governance & Quality (11%)
The smallest scored domain, but governance questions tend to be precise:
- Unity Catalog hierarchy — Catalog → Schema → Tables / Views / Volumes
- Table types — managed vs. external tables, when to use each
- Permissions model — GRANT, REVOKE, ownership, access control on catalogs, schemas, tables
- External locations — connecting Unity Catalog to cloud storage
- Data quality enforcement via Lakeflow expectations
Domain 3 (Data Processing & Transformations) at 31% is where most points live. Domain 2 (Development & Ingestion) at 30% is close behind. Together they account for 61% of the exam — prioritize Delta Lake, Lakeflow, Auto Loader, and Spark SQL.
How to Study: 4–6 Week Plan
This plan assumes you have some Spark and Databricks experience. Candidates completely new to the platform should add 2–4 weeks and start with the free Databricks Community Edition.
Week 1: Exam Guide Review and Gap Analysis
Goal: Understand the scope and identify weak areas.
- Download and read the official exam guide (PDF). Map every topic to your current knowledge.
- Sign up for Databricks Academy (free tier) and browse the recommended training paths.
- If you don't have a Databricks workspace, sign up for Databricks Community Edition — it's free and supports notebooks with Spark.
- Focus the first week on understanding the Medallion Architecture and Delta Lake fundamentals if they're new to you.
Weeks 2–3: Core Training and Hands-On Labs
Goal: Work through the official training with hands-on practice.
Complete the Databricks Academy self-paced courses:
- Data Ingestion with Lakeflow Connect — Auto Loader, file ingestion patterns
- Build Data Pipelines with Lakeflow Spark Declarative Pipelines — pipeline definitions, expectations, materialized views
- Deploy Workloads with Lakeflow Jobs — Workflows, Asset Bundles, scheduling
- DevOps Essentials for Data Engineering — CI/CD, version control, deployment
Parallel practice in your workspace:
- Create a Bronze → Silver → Gold pipeline using Delta Lake
- Configure Auto Loader to ingest files from a cloud storage path
- Set up a Lakeflow Declarative Pipeline with data quality expectations
- Create and schedule a Databricks Workflow with multiple tasks
Weeks 4–5: Practice Questions and Weak Area Review
Goal: Simulate exam conditions and close remaining gaps.
- Take practice exams from Databricks Academy or reputable third-party providers.
- For every wrong answer, trace it back to the exam guide topic and review the relevant training module.
- Create flashcards for common decision points:
- "When to use Auto Loader vs. COPY INTO"
- "Managed vs. external table in Unity Catalog"
- "Materialized view vs. streaming table in Lakeflow"
- "When to use OPTIMIZE vs. Z-ORDER vs. VACUUM"
- Review Unity Catalog permissions model — GRANT/REVOKE syntax, catalog hierarchy, ownership.
Week 6: Final Review and Exam
Goal: Consolidate and pass.
- Re-read the exam guide. Can you explain every topic listed?
- Run through your flashcards one final time.
- Register for the exam on Webassessor at least 1 week in advance.
- If taking online: run the Kryterion system check to verify your machine meets technical requirements.
- The night before: review Delta Lake operations and Lakeflow pipeline syntax. Get 8 hours of sleep.
I'm preparing for the Databricks Certified Data Engineer Associate exam. My background: - Current role: [YOUR ROLE] - Months of Databricks experience: [X] - Tools I use daily: [LIST — e.g., Spark SQL, PySpark, Delta Lake, Databricks Workflows] - Tools I've never used: [LIST — e.g., Lakeflow Declarative Pipelines, Unity Catalog, Auto Loader] - Hours per week I can study: [X] - Target exam date: [DATE] Based on the exam domains: - Databricks Intelligence Platform (10%) - Development and Ingestion (30%) - Data Processing & Transformations (31%) - Productionizing Data Pipelines (18%) - Data Governance & Quality (11%) Create a week-by-week study schedule that: 1. Prioritizes Domain 2 and Domain 3 (61% of the exam combined) 2. Front-loads my weakest areas 3. Includes specific Databricks Academy courses and hands-on lab exercises 4. Reserves the final week for practice exams only
The official Databricks Academy courses are the best preparation resource. Combine them with hands-on lab work in a Databricks workspace. The most common mistake is studying Spark theory without actually building pipelines.
Best Study Resources (Ranked)
Free Resources
Paid Resources
Common Study Mistakes
- Studying generic Spark documentation instead of Databricks-specific features — the exam tests Delta Lake, Lakeflow, and Unity Catalog, not vanilla Spark
- Skipping Unity Catalog because it's only 11% — governance questions are precise and easy to lose points on
- Not practicing Auto Loader syntax — it's a major topic in Domain 2 and requires hands-on familiarity
- Memorizing answers from brain dumps — Databricks rotates questions and dumps often contain wrong answers
- Ignoring Databricks Asset Bundles (DAB) — this is a new topic that replaced earlier deployment methods and is actively tested
How to Register and Schedule the Exam
Create a Webassessor Account
Go to webassessor.com/databricks and click Register to create an account. Use your personal email — this is where your exam results and certification badge will be sent.
Find the Data Engineer Associate Exam
After logging in, click Register for an Exam in the left menu. Search for Databricks Certified Data Engineer Associate and click Register.
Choose Delivery Method
Select between:
- Online proctored — you'll take the exam at home through Kryterion. Before booking, run the Kryterion system check to verify your webcam, microphone, and internet connection meet requirements.
- Test center — select a Kryterion testing center near you and pick a date/time.
Pay and Confirm
The exam costs $200 USD (plus applicable taxes). Pay by credit card during registration. You'll receive a confirmation email with your exam appointment details and instructions.
Prepare Your Setup (Online Proctored)
On exam day:
- Close all background applications
- Ensure a clean desk — no papers, notes, or second monitors
- Have your government-issued photo ID ready (passport, driver's license)
- Log in to Webassessor 15 minutes before your appointment time
- Register for the exam: webassessor.com/databricks
- Kryterion system check (online proctored): kryterion.com/systemcheck
- Kryterion technical requirements: Online Testing Requirements
- Certification FAQ: databricks.com/learn/certification/faq
- Access your earned credentials: credentials.databricks.com
Databricks vs AWS DEA-C01 vs Microsoft DP-700 — Which Should You Get?
Which One First?
- If your team uses Databricks: Start here. The exam maps directly to your daily work — Delta Lake, Workflows, Unity Catalog. It's also the shortest exam (90 minutes).
- If job postings mention AWS services (Glue, Redshift, S3): Start with AWS DEA-C01. AWS leads the cloud market in data engineering job postings.
- If your company runs on Microsoft Fabric: Start with Microsoft DP-700. Enterprise finance, healthcare, and government organizations lean Microsoft.
- Best two-cert combo: Databricks DE Associate + AWS DEA-C01. Databricks runs on AWS, Azure, and GCP — pairing it with a cloud cert gives the broadest coverage. Many teams use Databricks on AWS specifically.
For the complete breakdown of all data engineering certifications — including Google Cloud Professional Data Engineer and dbt — see our Best Data Engineering Certifications guide.
Databricks DE Associate is the best certification for the lakehouse ecosystem. It pairs well with a cloud cert (AWS or Azure) since Databricks runs on top of those clouds. Get the one that matches your target job postings first.
How to Add This Certification to Your Resume and LinkedIn
Resume
- Certifications section: List as "Databricks Certified Data Engineer Associate" with the year earned.
- Technical Skills: Include "Databricks," "Delta Lake," "Unity Catalog," and "Apache Spark" as platform/tool skills.
- ATS keywords: Include "Databricks Certified," "Delta Lake," and "Lakeflow" — recruiters filter on these terms.
- Certifications feature: Go to Profile → Add section → Licenses & Certifications. Use the official name: "Databricks Certified Data Engineer Associate." Link to your Databricks credential page.
- Headline update: Example: "Data Engineer | Databricks Certified | Spark, Delta Lake, Python."
- Share your badge: Databricks issues a digital badge via Credly. Share it on LinkedIn for a verified, clickable credential.
For the complete guide on structuring your data engineer resume — including the bullet formula, ATS keywords, and templates by seniority — see our Data Engineer Resume Guide.
List the certification with its full official name on your resume. On LinkedIn, share the Credly badge for a verified credential. Include "Delta Lake" and "Unity Catalog" in your skills — they're increasingly used as recruiter search filters.
- Read the official exam guide PDF and mapped all topics to your knowledge level
- Completed the Databricks Academy self-paced courses for all 5 domains
- Built a Bronze → Silver → Gold pipeline using Delta Lake in a Databricks workspace
- Configured Auto Loader to ingest files incrementally and handle schema evolution
- Created and scheduled a Databricks Workflow with multi-task dependencies
- Written Lakeflow Declarative Pipeline code with data quality expectations
- Practiced Unity Catalog permissions: GRANT, REVOKE, managed vs. external tables
- Know the difference between OPTIMIZE, Z-ORDER, and VACUUM for Delta tables
- Taken at least one full practice exam under timed conditions (90 minutes)
- Registered on Webassessor and run the Kryterion system check (if taking online)
Key Takeaways
- 1The Databricks DE Associate is a 45-question, 90-minute exam costing $200 USD — pass/fail scoring, valid for 2 years.
- 2Domain 2 (Development & Ingestion) and Domain 3 (Data Processing & Transformations) account for 61% of the exam — prioritize Delta Lake, Auto Loader, and Lakeflow.
- 3The certification is most valuable for engineers working with Databricks, Spark, or the lakehouse architecture.
- 4Databricks Academy self-paced courses are the best preparation — they're free, official, and hands-on.
- 5Code on the exam is in SQL when possible, Python otherwise. No Scala required.
- 6Best paired with a cloud cert (AWS DEA-C01 or Microsoft DP-700) for maximum job market coverage.
Frequently Asked Questions
Do I need to know Scala for the Databricks DE Associate exam?
No. Code on the exam is provided in SQL when possible. Where SQL cannot express the concept (e.g., Auto Loader configuration, PySpark DataFrame operations), code is in Python. Scala is not tested.
Is there a Databricks Data Engineer Professional certification?
Yes. Databricks also offers a Data Engineer Professional certification for more experienced engineers. It covers advanced topics like performance tuning, complex pipeline architectures, and production-grade deployment. The Associate is the recommended starting point.
Can I use Databricks Community Edition to study?
Yes, for most topics. Community Edition supports Spark notebooks, Delta Lake operations, and SQL queries. However, some features like Unity Catalog, Lakeflow Declarative Pipelines, and Databricks Workflows require a full workspace (trial or paid). Use a Databricks trial for those topics.
How does recertification work?
The Databricks DE Associate certification expires after 2 years. To recertify, you must take and pass the current version of the exam (full $200 fee). There is no discounted renewal assessment like Microsoft offers.
Should I get the Databricks cert or AWS DEA-C01 first?
Get the one that matches your current or target tech stack. If you use Databricks daily, start there — it's the shortest exam (90 minutes) and maps directly to your work. If your target roles are AWS-centric, start with DEA-C01. For maximum coverage, plan to get both within 6 months.
What is the difference between Delta Live Tables and Lakeflow Declarative Pipelines?
They are the same product. Databricks renamed Delta Live Tables (DLT) to Lakeflow Declarative Pipelines in 2024–2025 as part of the broader Lakeflow product family. The exam guide uses the new name (Lakeflow), but some documentation and community posts still reference DLT.
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Sources & References
- Databricks Certified Data Engineer Associate — Databricks (2026)
- Databricks Certified Data Engineer Associate Exam Guide — Databricks (2025)
- Designing Data-Intensive Applications — Martin Kleppmann (2017)