Databricks Certified Data Engineer Professional Quick Facts (2025)

Databricks Certified Data Engineer Professional Exam Overview

Studying for the Databricks Certified Data Engineer Professional exam can be overwhelming due to its advanced technical depth. This exam guide simplifies everything so you can confidently prepare, focus on what matters most, and pass with ease.

What is the Databricks Certified Data Engineer Professional Certification?

The Databricks Certified Data Engineer Professional certification validates your expertise in performing advanced data engineering tasks using the Databricks Lakehouse Platform. This includes hands-on experience building efficient data pipelines, modeling complex datasets, utilizing key tools like Apache Spark and Delta Lake, and applying best practices for security, monitoring, and deployment.

This expert-level credential is ideal for experienced practitioners seeking to prove their ability to design production-grade data engineering workflows on Databricks.

Who Is This Certification For?

This certification is tailored for:

• Seasoned data engineers and architects working with data pipelines on the Databricks platform
• Cloud professionals with experience in Spark, Delta Lake, and Python
• Data veterans transitioning into more complex lakehouse development or streaming workloads
• Engineering teams looking to upskill and certify professionals in best-practice architectures

If you already have a foundational understanding of Databricks, this exam helps demonstrate your readiness to deliver enterprise-grade solutions.

What Jobs Can I Get with This Certification?

Passing the Databricks Certified Data Engineer Professional certification can help you land or grow in roles such as:

• Senior or Lead Data Engineer
• Cloud Data Architect
• Big Data Engineer
• ETL Developer
• Data Platform Engineer
• Data Ops Engineer
• Analytics Engineer
• ML/AI Pipeline Engineer

It’s a powerful credential for organizations using Databricks in production, especially across industries embracing Lakehouse architecture.

What version of the exam should I take?

Currently, there is only one version of the Databricks Certified Data Engineer Professional exam. There are no role-based variations or editions.

How much does the exam cost?

The registration fee for this advanced exam is $200 USD. Please note that applicable taxes may apply based on your local jurisdiction. Databricks occasionally offers discounts through bundles or offers in their certification portal.

How many questions are on the exam?

The certification exam consists of 60 multiple-choice questions, which may include unscored items used for statistical analysis. These unscored questions do not count toward your final score.

How long is the exam?

You will have 120 minutes (2 hours) to complete the exam. The time limit includes extra time to account for any potential unscored content.

What language is the exam delivered in?

The Databricks Certified Data Engineer Professional exam is currently only available in English.

What’s the passing score?

Databricks does not publicly share the exact passing score, but candidates are expected to demonstrate advanced proficiency. Success typically requires a combination of hands-on experience and thorough preparation.

Is the exam difficult?

Yes, this is an advanced-level certification focused heavily on practical, scenario-based applications of Databricks features. It challenges your knowledge of:

• Apache Spark performance tuning
• Structured Streaming
• Delta Lake transaction handling
• Lakehouse data modeling
• Security and governance practices
• Testing and deploying jobs through CLI, API, and notebooks

Most candidates find that realistic Databricks Certified Data Engineer Professional practice exams are a must to properly simulate the exam's difficulty and style.

What topics does the exam cover?

The exam is structured into six weighted domains:

1. Databricks Tooling (20%)

   • Delta Lake internals (transaction log, object storage)
   • Optimistic concurrency
   • Partitioning and indexing (Z-order, Bloom filters)
   • Table format optimizations

2. Data Processing (30%)

   • Batch and streaming ETL pipelines
   • Structured Streaming patterns
   • Deduplication logic
   • Change Data Feed (CDF) workflows
   • Incremental processing

3. Data Modeling (20%)

   • Bronze → Silver transformations
   • Slowly Changing Dimensions (SCD Types 0, 1, 2)
   • Schema enforcement
   • Lookup tables and normalization strategies
   • Table constraints

4. Security & Governance (10%)

   • Row- and column-level access controls using dynamic views
   • Data masking and role-based policies

5. Monitoring & Logging (10%)

   • Spark UI and cluster diagnostics (Ganglia, timelines)
   • Event metrics
   • SLA-based production monitoring

6. Testing & Deployment (10%)

   • Job creation via CLI/REST API
   • Multi-task workflows
   • Repairing failed jobs
   • Managing Python wheel dependencies

Are there any prerequisites?

There are no strict prerequisites. However, Databricks strongly recommends:

• Minimum 1 year of hands-on experience with Databricks, Spark, Delta Lake, and structured streaming
• Familiarity with Databricks Workspace, Notebooks, Jobs, REST API, and CLI
• Proficiency in Python and basic SQL (SQL is used for Delta Lake functionality)
• Completion of the self-paced or instructor-led “Advanced Data Engineering with Databricks” training course

Key Skills to Master Before the Exam

To maximize your chances of passing, focus on:

1. Delta Lake Design and Operations

   • Transactionality, schema evolution, CDC with CDF
   • Cloning techniques and table constraints

2. Optimized Data Pipelines

   • Partitioning, coalescing, streaming joins
   • CDC-triggered updates and deletions

3. Streaming and Batch Workflows

   • Stream-static joins
   • De-duplication logic
   • SCD implementation in real-time pipelines

4. Tooling and Deployment

   • CLI-based deployments and REST API automation
   • Job orchestration using notebooks and wheel packages

5. Monitoring and Debugging

   • Performance tuning via the Spark UI
   • Identifying bottlenecks at stage and job levels

6. Security and Governance

   • Implementing table ACLs and data masking strategies via dynamic views

Common Mistakes to Avoid

Candidates often report their biggest mistakes as:

• Neglecting structured streaming concepts and real-time data flow design
• Not testing with real-life scenario questions under time pressure
• Focusing too much on Spark basics while ignoring Databricks-specific tooling
• Ignoring notebook dependency deployment or Python project structure (e.g., wheels)
• Forgetting to review the CLI and REST API usage patterns
• Underestimating governance and data constraints in Lakehouse tables

Using professionally authored Databricks Data Engineer Professional practice exams can help ensure you're minimizing these risks by targeting exactly what Databricks tests.

How Long Is the Certification Valid?

The Databricks Certified Data Engineer Professional certification is valid for 2 years from the issue date. After that, you'll need to recertify by taking the current live version of the full exam (there is no shorter recertification exam at this time).

How to Maintain Your Certification

To maintain active certification status:

1. Track your expiration date and plan to sit for recertification in time
2. Stay updated with the latest Databricks Lakehouse and Delta Lake features
3. Follow release notes and official training updates from Databricks
4. Retake the full exam every 2 years, as required

What’s Next After This Exam?

If you’ve passed the Data Engineer Professional exam, you’ve demonstrated Databricks fluency and senior engineering capability. You may want to pursue:

• Databricks Certified Machine Learning Professional
• Databricks Generative AI Engineer Associate Certification
• Architect certifications or domain-specific advanced credentials depending on your career path.

This certification is often regarded as the most technically rigorous Databricks credential, and it opens doors to lead data architecture or platform engineering roles.

Where Can I Take the Exam?

You can take the exam online via a proctored delivery method. To register:

1. Go to the official Databricks Data Engineer Professional certification page
2. Review exam logistics and requirements
3. Run the system check to ensure your equipment and internet connection is compatible
4. Register and select your testing window

What If I Don’t Pass the Exam?

If you don’t pass on the first try:

• Review your score report to identify weak areas
• Go back through the exam guide to target those domains
• Improve your hands-on experience with notebooks, job orchestration, or performance tuning
• Reattempt after further preparation—there’s no mandatory cooling-off period, but it’s advised to study fully before retaking

Additional Resources

To maximize your preparation time, leverage:

• Official Databricks training courses (instructor-led or on-demand)
• Sample code, notebooks, and repos available in Databricks Academy
• Practice working with Spark UI, Z-ordering, Delta CDF, and job APIs
• Join forums, Slack groups, or communities for tips and support

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The Databricks Certified Data Engineer Professional exam is challenging—but with strategic preparation, real-world experience, and reliable study tools, it's absolutely achievable. Good luck as you pursue this valuable credential and advance your career in the Lakehouse data ecosystem!