DATABRICKS

Databricks Certified Data Engineer Professional

DATA-ENGINEER-PROFESSIONAL

The Databricks Certified Data Engineer Professional certification validates your advanced skills in building, optimizing, and maintaining production-grade data engineering solutions on the Databricks Data Intelligence Platform. It is designed for experienced data engineers who architect secure, reliable, and cost-effective ETL pipelines, implement streaming workloads, and orchestrate workflows using Python and SQL. Earning this credential demonstrates your ability to deliver complex data engineering projects at scale.

Practice Free Questions

10Domains

26Objectives

134Practice pages

639Free questions

DATA-ENGINEER-PROFESSIONAL free practice questions

Choose any objective and open any question page — no sign-in required.

Using Python and Tools for development

23 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–23

Building and Testing an ETL pipeline with Lakeflow Spark Declarative Pipelines, SQL, and Apache Spark on the Databricks Platform

29 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–29

Design and implement data ingestion pipelines to efficiently ingest a variety of data formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from diverse sources such as message buses and cloud storage.

34 questions · 7 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–34

Create an append-only data pipeline capable of handling both batch and streaming data using Delta.

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

Write efficient Spark SQL and PySpark code to apply advanced data transformations, including window functions, joins, and aggregations, to manipulate and analyze large Datasets.

20 questions · 4 pages

Questions 1–5 6–10 11–15 16–20

Develop a quarantining process for bad data with Lakeflow Spark Declarative Pipelines, or autoloader in classic jobs.

19 questions · 4 pages

Questions 1–5 6–10 11–15 16–19

Configure Lakehouse Federation with proper governance across the supported source Systems.

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

Monitoring

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

Alerting

16 questions · 4 pages

Questions 1–5 6–10 11–15 16–16

Understand how / why using Unity Catalog managed tables reduces operations overhead and maintenance burden.

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

Understand delta optimization techniques, such as deletion vectors and liquid clustering.

29 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–29

Understand the optimization techniques used by Databricks to ensure the performance of queries on large datasets (data skipping, file pruning, etc.).

30 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30

Apply Change Data Feed (CDF) to address specific limitations of streaming tables and enhance latency.

15 questions · 3 pages

Questions 1–5 6–10 11–15

Use the query profile to analyze the query and identify bottlenecks, such as bad data skipping, inefficient types of joins, and data shuffling.

17 questions · 4 pages

Questions 1–5 6–10 11–15 16–17

Applying Data Security mechanisms.

23 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–23

Ensuring Compliance

28 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–28

Create and add descriptions/metadata about enterprise data to make it more discoverable.

30 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30

Demonstrate understanding of Unity Catalog permission inheritance model.

18 questions · 4 pages

Questions 1–5 6–10 11–15 16–18

Debugging and Troubleshooting

34 questions · 7 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–30 31–34

Deploying CI/CD

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

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Percentages reflect share of the current practice bank, not official exam weightings. Every page above is a live, crawlable practice page (26 objectives · 134 pages).

Databricks Certified Data Engineer Professional

DATA-ENGINEER-PROFESSIONAL free practice questions

Using Python and Tools for development

Building and Testing an ETL pipeline with Lakeflow Spark Declarative Pipelines, SQL, and Apache Spark on the Databricks Platform

Design and implement data ingestion pipelines to efficiently ingest a variety of data formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, Text and Binary from diverse sources such as message buses and cloud storage.

Create an append-only data pipeline capable of handling both batch and streaming data using Delta.

Write efficient Spark SQL and PySpark code to apply advanced data transformations, including window functions, joins, and aggregations, to manipulate and analyze large Datasets.

Develop a quarantining process for bad data with Lakeflow Spark Declarative Pipelines, or autoloader in classic jobs.

Configure Lakehouse Federation with proper governance across the supported source Systems.

Monitoring

Alerting

Understand how / why using Unity Catalog managed tables reduces operations overhead and maintenance burden.

Understand delta optimization techniques, such as deletion vectors and liquid clustering.

Understand the optimization techniques used by Databricks to ensure the performance of queries on large datasets (data skipping, file pruning, etc.).

Apply Change Data Feed (CDF) to address specific limitations of streaming tables and enhance latency.

Use the query profile to analyze the query and identify bottlenecks, such as bad data skipping, inefficient types of joins, and data shuffling.

Applying Data Security mechanisms.

Ensuring Compliance

Create and add descriptions/metadata about enterprise data to make it more discoverable.

Demonstrate understanding of Unity Catalog permission inheritance model.

Debugging and Troubleshooting

Deploying CI/CD

Design and implement scalable data models using Delta Lake to manage large datasets.

Simplify data layout decisions and optimize query performance using Liquid Clustering.

Identify the benefits of using liquid Clustering over Partitioning and ZOrder.

Design Dimensional Models for analytical workloads, ensuring efficient querying and aggregation.

Databricks Certified Data Engineer Professional

DATA-ENGINEER-PROFESSIONAL free practice questions

Domain 1 · Developing Code for Data Processing using Python and SQL8% of practice bank · 2 objectives · 52 questions8%

Domain 2 · Data Ingestion & Acquisition9% of practice bank · 2 objectives · 58 questions9%

Domain 3 · Data Transformation, Cleansing, and Quality6% of practice bank · 2 objectives · 39 questions6%

Domain 4 · Data Sharing and Federation12% of practice bank · 3 objectives · 77 questions12%

Domain 5 · Monitoring and Alerting7% of practice bank · 2 objectives · 43 questions7%

Domain 6 · Cost & Performance Optimization18% of practice bank · 5 objectives · 116 questions18%

Domain 7 · Ensuring Data Security and Compliance8% of practice bank · 2 objectives · 51 questions8%

Domain 8 · Data Governance8% of practice bank · 2 objectives · 48 questions8%

Domain 9 · Debugging and Deploying9% of practice bank · 2 objectives · 59 questions9%

Domain 10 · Data Modeling15% of practice bank · 4 objectives · 96 questions15%