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Databricks : CERTIFIED-DATA-ENGINEER-PROFESSIONAL

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Oct 19,2026
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Certified Data Engineer Professional
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About CERTIFIED-DATA-ENGINEER-PROFESSIONAL Exam


Prepare for the Databricks Certified Data Engineer Professional Exam and validate your advanced skills in designing, building, and optimizing complex data pipelines on the Databricks Lakehouse platform using Apache Spark. This certification is ideal for senior data engineers, analytics engineers, and technical leads responsible for large-scale data processing, performance optimization, and architecture best practices.
Recommend you to use our Databricks Certified Data Engineer Professional actual test practice material latest version to ensure best practices and first-attempt pass guaranteed!
— Exam Topics
Advanced Spark Programming & Optimization (20%)
Data Engineering & ETL/ELT at Scale (25%)
Delta Lake & Lakehouse Architecture (20%)
Data Governance, Security & Access Control (15%)
Performance Tuning & Monitoring (10%)
CI/CD Pipelines, Job Scheduling & Orchestration (10%)
Databricks Certified Data Engineer Professional Exam Format
— Exam Format:
Exam code- DCEP
Exam type- Proctored (Online)
Exam duration- 120 minutes
Exam length- 60 multiple-choice/multiple-select questions
Question types- Multiple choice & multiple select
Passing score- 70%
Delivery languages- English
Additional study materials – Free learning path (Post Premium Access, you can ask Clearcatnet for the free learning path link)
Exam Level- Professional / Advanced Data Engineer-level
Role- Senior Data Engineer / Big Data Architect / Analytics Engineer / Databricks Developer
Renewal Frequency- Every 3 years (recommended to stay updated with Databricks platform and Spark updates)
CERTIFIED-DATA-ENGINEER-PROFESSIONAL Exam FAQ

Q1: What is the Databricks Certified Data Engineer Professional exam, including duration and questions?

Exam Name: Databricks Certified Data Engineer Professional
Level: Professional
Duration: 120 minutes
Questions: 59 scored multiple-choice questions
Question Format: Multiple choice
Role: Professional Data Engineer / Databricks Data Engineer
Main Topics: Python and SQL data processing, data ingestion and acquisition, data transformation and quality, data sharing and federation, monitoring and alerting, cost and performance optimization, security and compliance, data governance, debugging and deployment, and data modeling.

Q2: What skills does the Databricks Certified Data Engineer Professional certification validate?

The certification validates advanced skills in building, optimizing, securing, deploying, and maintaining production-grade data pipelines on the Databricks Data Intelligence Platform. Candidates should understand Python, SQL, Apache Spark, Delta Lake, Unity Catalog, incremental data processing, and production pipeline operations.

Q3: What are the main topics covered in the Databricks Certified Data Engineer Professional exam?

The main topics covered in the exam include developing code for data processing using Python and SQL, data ingestion and acquisition, data transformation, cleansing and quality, data sharing and federation, monitoring and alerting, cost and performance optimization, data security and compliance, data governance, debugging and deploying, and data modeling. Practical knowledge of Delta Lake, Lakeflow, Unity Catalog, and production data pipelines is important.

Q4: What prerequisites and experience are recommended for the Databricks Certified Data Engineer Professional exam?

There are no formal prerequisites for taking the exam. Databricks recommends practical experience building production data pipelines and completing relevant data engineering training. Candidates should be comfortable with Python, SQL, Apache Spark, Delta Lake, data pipeline optimization, access controls, monitoring, and deployment practices.

Q5: How can I prepare for the Databricks Certified Data Engineer Professional exam?

Review the official exam guide and practise building batch and streaming pipelines using Python and SQL. Study Delta Lake, Lakeflow, Unity Catalog, data quality, performance tuning, security, monitoring, and CI/CD deployment. Work through production-style data engineering scenarios and review your weak areas. Use Clearcatnet PDF study materials and practice questions to reinforce your understanding and improve exam readiness.

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