Exam Name: Databricks Certified Data Engineer Professional
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Updated: Oct 21, 2026
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- Total Questions: 250
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Databricks Certified-Data-Engineer-Professional Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Professional |
| Exam Number: | Certified Data Engineer Professional |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Passing Score: | Not publicly specified in the current official exam guide |
| Certificate Validity Period: | 2 years |
| Exam Price: | USD 200, plus applicable taxes as required by local law |
| Available Languages: | English |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 59 scored questions |
| Exam Format: | Multiple-choice |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or test center proctored |
| Pre Condition: | No mandatory prerequisite. Databricks recommends related course attendance and approximately one year of hands-on experience performing the Data Engineering tasks covered by the exam. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-professional |
Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Sharing and Federation | - Delta Sharing
|
| Topic 2: Data Modelling | - Scalable Data Models
|
| Topic 3: Cost & Performance Optimisation | - Delta Optimization
|
| Topic 4: Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Topic 5: Monitoring and Alerting | - Monitoring
|
| Topic 6: Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Topic 7: Data Governance | - Unity Catalog Permissions
|
| Topic 8: Debugging and Deploying | - Debugging and Troubleshooting
|
| Topic 9: Ensuring Data Security and Compliance | - Data Security
|
| Topic 10: Data Transformation, Cleansing, and Quality | - Advanced Data Transformation
|
Certified-Data-Engineer-Professional Exam Questions — Answered Honestly
The Databricks Certified Data Engineer Professional exam requires Not publicly specified in the current official exam guide to pass, and registration costs USD 200, plus applicable taxes as required by local law. A failed attempt means paying the same fee again — a readiness check with the Actual4dump 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 Actual4dump 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 Actual4dump'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.
Actual4dump 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, Actual4dump delivers your practice questions within 1 minute — preparation starts the same day.
The Databricks Certified Data Engineer Professional exam contains 59 scored questions to complete within 120 minutes. Timed simulation in the Actual4dump Desktop Test Engine teaches that rhythm before exam day does.
The official Databricks Certified Data Engineer Professional outline defines 10 domains. The leading three are Data Sharing and Federation, Ensuring Data Security and Compliance, and Data Ingestion & Acquisition. The full list is in the topics section above, and Actual4dump'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 mandatory prerequisite. Databricks recommends related course attendance and approximately one year of hands-on experience performing the Data Engineering tasks covered by the exam. 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 engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?
- A. Reject, as they should use {{job.trigger_time.is_weekday}} instead.
- B. Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
- C. Merge, as the job configuration looks good.
- D. Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
Correct Answer: B 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?
- A. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
- B. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
- C. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
- D. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
Correct Answer: C 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?
- A. They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
- B. They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
- C. They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
- D. They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
Correct Answer: A 🗳️
Explanation: Only visible for Actual4dump members. You can sign-up / login (it's free).
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".
The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?
- A.
- B.
- C. preds.write.mode("append").saveAsTable("churn_preds")
- D. preds.write.format("delta").save("/preds/churn_preds")
- E.
Correct Answer: C 🗳️
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.
- A. to_interval("event_time", "5 minutes").alias("time")
- B. lag("event_time", "10 minutes").alias("time")
- C. "event_time"
- D. window("event_time", "10 minutes").alias("time")
- E. window("event_time", "5 minutes").alias("time")
Correct Answer: E 🗳️
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