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Last Updated: Sep 09, 2026
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Professional Exam |
| Exam Number: | Databricks-Certified-Data-Engineer-Professional |
| Exam Format: | Multiple-choice, Multiple-response |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Exam Price: | USD 200 |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 60 |
| Certificate Validity Period: | 2 years |
| Passing Score: | 70 |
| Available Languages: | English |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored |
| Pre Condition: | Databricks Certified Data Engineer Associate certification |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-professional |
Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modeling and Storage | 20% | - File Formats - Data Modeling - Storage Optimization |
| Topic 2: Data Processing | 28% | - Spark SQL - ETL Pipelines - Data Transformation - Structured Streaming |
| Topic 3: Monitoring and Troubleshooting | 16% | - Troubleshooting - Monitoring - Performance Optimization |
| Topic 4: Databricks Lakehouse Platform | 24% | - Unity Catalog - Lakehouse Architecture - Data Management - Delta Lake |
| Topic 5: Data Quality and Governance | 12% | - Data Lineage - Data Quality - Governance |
Databricks Databricks-Certified-Data-Engineer-Professional Exam: Your Questions, Answered
The Databricks Certified Data Engineer Professional exam is the official Databricks assessment behind the Databricks Certification credential, which sits at the Professional level. Passing it confirms that your skills meet the vendor's current requirements rather than a textbook outline. It also connects with related certifications such as Databricks Certified Data Engineer Associate, so it can anchor a broader certification path.
According to the official exam information, the Databricks Certified Data Engineer Professional exam includes 60 questions and gives you 120 minutes to complete them. Treat that as a pacing exercise, not just a knowledge check: bank the questions you know first, flag the ones that stall you, and circle back instead of burning minutes on a single item. Before test day, run at least one full timed session in the Actual4Cert desktop or online test engine, so the clock never feels unfamiliar when it counts.
To pass the Databricks Certified Data Engineer Professional exam you need 70, and the official registration fee is USD 200. Keep one thing in mind: a failed attempt is not discounted, so retaking the exam means paying USD 200 again in full. A practical safeguard is to sit a complete Actual4Cert practice test a week or two before your exam date; if your timed scores are not sitting comfortably above the passing mark, consider pushing your booking back and drilling the weak domains first.
The official prerequisites for the Databricks Certified Data Engineer Professional exam are as follows: Databricks Certified Data Engineer Associate certification. Requirements can change over time, so confirm the details on the official Databricks exam page at https://www.databricks.com/learn/certification/data-engineer-professional before you book your seat.
Yes. A free PDF demo of the Databricks Certified Data Engineer Professional practice questions is available on the Actual4Cert samples page, so you can check the question style and difficulty before spending anything. Every purchase also includes 365 days of free updates, and if your product expires after that period, you can extend the update service from your member zone at 50% off.
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The Databricks Certified Data Engineer Professional syllabus is organized into 5 domains. The leading areas include Databricks Lakehouse Platform (24%), Data Modeling and Storage (20%), and Data Processing (28%). For the complete, topic-by-topic breakdown, scroll up to the Exam Topics section above.
Databricks Certified Data Engineer Professional Sample Questions:
While reviewing a query's execution in the Databricks Query Profiler, a data engineer observes that the Top Operators panel shows a Sort operator with high Time Spent and Memory Peak metrics. The Spark UI also reports frequent data spilling. How should the data engineer address this issue?
- A. Increase the number of shuffle partitions to better distribute data.
- B. Repartition the DataFrame to a single partition before sorting.
- C. Convert the sort operation to a filter operation.
- D. Switch to a broadcast join to reduce memory usage.
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A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.
In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.
The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?
- A. Move the table definition into a separate function, and make calls to this function using different input parameters inside the for loop.
- B. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
- C. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
- D. Wrap the for loop inside another table definition, using generalized names and properties to replace with those from the inner table definition.
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A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?
- A. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
- B. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
- C. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
- D. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
- E. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
Explanation: Only visible for Actual4Cert members. You can sign-up / login (it's free).
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline. Which command should the data engineer enter in a web terminal configured with the Databricks CLI?
- A. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
- B. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
- C. Stop the existing pipeline; use the returned settings in a reset command
- D. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
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A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?
- A. Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.
- B. Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.
- C. Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
- D. Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.
- E. Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.
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