Sharpen the saw before you cut. The Databricks Certified Data Engineer Professional exam rewards candidates who prepare with the right tool, and PracticeVCE built one: 250 practice questions in three formats, refined through years of painstaking revision.
Databricks Certified-Data-Engineer-Professional Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Professional |
| Exam Number: | Databricks-Certified-Professional-Data-Engineer |
| Exam Format: | Multiple-choice, Scenario-based |
| Passing Score: | Not publicly disclosed; commonly estimated ~70% |
| Certificate Validity Period: | 2 years |
| Exam Price: | USD 200 (plus applicable taxes) |
| Exam Duration: | 120 minutes |
| Available Languages: | English, Korean, Portuguese (Brazil), Japanese |
| Real Exam Qty: | 59 (scored); may include unscored items |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Recommended Training: | Self-paced courses on Databricks Academy Instructor-led: Advanced Data Engineering With Databricks |
| Exam Registration: | Databricks Certification Portal (Webassessor) |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or test center proctored |
| Pre Condition: | No formal prerequisites; completion of Databricks Certified Data Engineer Associate and 1+ year of hands-on data engineering experience on Databricks are highly recommended |
| Official Syllabus URL: | https://www.databricks.com/certification/data-engineer-professional |
Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing Code for Data Processing using Python and SQL | ~22% | - Manage dependencies, libraries, and UDFs - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Implement scalable Python/SQL code and project structures |
| Monitoring, Logging, and Troubleshooting | ~8% | - Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables |
| Data Transformation, Cleansing, and Quality | ~12% | - Apply advanced Spark transformations - Enforce data quality and quarantine bad data |
| Cost and Performance Optimization | ~13% | - Optimize queries, clusters, and storage - Leverage system tables and observability tools |
| Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Security and Governance | ~10% | - Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance |
| Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
Certified-Data-Engineer-Professional Exam FAQ: The Details That Matter
The Databricks Certified Data Engineer Professional exam requires Not publicly disclosed; commonly estimated ~70% to pass, and registration costs USD 200 (plus applicable taxes). A failed attempt means paying the same fee again — a readiness check with the PracticeVCE engines before booking is the cheaper option.
Databricks recommends these training resources for the Databricks Certified Data Engineer Professional exam:
Follow the training with steady self-testing — the 250 practice questions from PracticeVCE help you apply theory and fill any gaps the training leaves.
The Certified-Data-Engineer-Professional exam is a Databricks certification exam validating the Databricks Certified Data Engineer Professional syllabus shown above. It belongs to these credential paths: Databricks Certified Data Engineer Associate. Candidates in 2026 prepare for it with PracticeVCE's 250 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
PracticeVCE issues a full refund if you take the Databricks Certified Data Engineer Professional exam within 60 days of purchase and do not pass. The policy does not apply if the exam is taken within 3 days of purchase, if the exam was never actually taken, or to free materials or expired orders, and the candidate name must match the payer name. Submit a scan of your enrollment slip and the official Score Report PDF within 2 days of the exam; claims are processed within 7 days. Alternatively, exchange for two free exam products of equal value and keep your original update service. Orders arrive by email within 1 minute — contact support if nothing arrives within 2 hours.
Online proctored or test center proctored Register for the Databricks Certified Data Engineer Professional exam through these official channels:
After booking, PracticeVCE delivers your practice questions within 1 minute — preparation starts the same day.
The Databricks Certified Data Engineer Professional exam contains 59 (scored); may include unscored items to complete within 120 minutes. Timed simulation in the PracticeVCE Desktop Test Engine teaches that rhythm before exam day does.
The official Databricks Certified Data Engineer Professional outline defines 9 domains. The leading three are Data Transformation, Cleansing, and Quality (~12%), Monitoring, Logging, and Troubleshooting (~8%), and Security and Governance (~10%). The full list is in the topics section above, and PracticeVCE's 250 practice questions cover each one.
Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows that simulates the real exam with two practice modes and works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 250 practice questions are identical across versions. Every purchase includes 365 days of free updates — whenever a new version is compiled, it is sent to you automatically — plus a 50% renewal discount afterward and unlimited computer installations.
No formal prerequisites; completion of Databricks Certified Data Engineer Associate and 1+ year of hands-on data engineering experience on Databricks are highly recommended Since requirements change over time, confirm the latest on the official Databricks exam page before scheduling.
Databricks Certified Data Engineer Professional Sample Questions:
A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?
- A. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
- B. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
- C. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
- D. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
Correct Answer: A 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?
- A. The globally unique ID of the newly triggered run.
- B. The job_id and number of times the job has been are concatenated and returned.
- C. The total number of jobs that have been run in the workspace.
- D. The number of times the job definition has been run in the workspace.
- E. The job_id is returned in this field.
Correct Answer: A 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
Which statement describes Delta Lake optimized writes?
- A. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
- B. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
- C. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
- D. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
Correct Answer: C 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
A data engineer needs to create an application that will collect information about the latest job run including the repair history. How should the data engineer format the request?
- A. Call/api/2.1/jobs/runs/get with the job_id and include_history parameters
- B. Call/api/2.1/jobs/runs/list with the run_id and include_history parameters
- C. Call/api/2.1/jobs/runs/get with the run_id and include_history parameters
- D. Call/api/2.1/jobs/runs/list with the job_id and include_history parameters
Correct Answer: D 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
A data engineer manages a production Lakeflow Declarative Pipeline that processes customer transaction data. The pipeline includes several data quality expectations such as transaction_amount > 0 and customer_id IS NOT NULL. These expectations are defined using the EXPECT clause in SQL.
The engineer aims to monitor the pipeline's data quality by analyzing the number of records that passed or failed each expectation during the latest pipeline update. The Lakeflow Declarative Pipelines event logs are stored in a Delta table named event_log_table.
For the most recent pipeline update, determine a programmatically appropriate approach to extract information like the name of each expectation, associated dataset, count of records that passed the expectation, and count of records that failed the expectation.
Which method retrieves the desired data quality metrics from the Lakeflow Declarative Pipelines event log?
- A. Use the Lakeflow Declarative Pipelines UI to navigate to the specific pipeline, select the dataset, and view the Data Quality tab to manually retrieve the expectation metrics.
- B. Query the event_log_table for events with event_type = 'data_quality' and directly select the passed_records and failed_records fields.
- C. Access the event_log_table, filter for events where event_type = 'expectation_result', and extract the expectation metrics from the details field.
- D. Access the event_log_table, filter for events where event_type = 'flow_progress', and parse details.flow_progress.data_quality.expectations field to extract the required metrics.
Correct Answer: C 🗳️
Explanation: Only visible for PracticeVCE members. You can sign-up / login (it's free).
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