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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: | Scenario-based, Multiple-choice |
| Real Exam Qty: | 59 (scored); may include unscored items |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Passing Score: | Not publicly disclosed; commonly estimated ~70% |
| Exam Duration: | 120 minutes |
| Available Languages: | English, Korean, Portuguese (Brazil), Japanese |
| Exam Price: | USD 200 (plus applicable taxes) |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Instructor-led: Advanced Data Engineering With Databricks Self-paced courses on Databricks Academy |
| Exam Registration: | Databricks Certification Portal (Webassessor) |
| Sample Questions: |  |
| 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 |
|---|---|---|
| Topic 1: Monitoring, Logging, and Troubleshooting | ~8% | - Use Spark UI, Query Profiler, and system tables - Diagnose common pipeline and job failures |
| Topic 2: Developing Code for Data Processing using Python and SQL | ~22% | - Manage dependencies, libraries, and UDFs - Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader |
| Topic 3: Security and Governance | ~10% | - Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance |
| Topic 4: CI/CD, Testing, and Deployment | ~6% | - Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines |
| Topic 5: Data Sharing and Federation | ~8% | - Configure Delta Sharing and Lakehouse Federation |
| Topic 6: Data Modeling | ~10% | - Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques |
| Topic 7: Cost and Performance Optimization | ~13% | - Leverage system tables and observability tools - Optimize queries, clusters, and storage |
| Topic 8: Streaming Workloads and Change Data Capture | ~11% | - Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines |
| Topic 9: Data Transformation, Cleansing, and Quality | ~12% | - Enforce data quality and quarantine bad data - Apply advanced Spark transformations |
Common Questions About the Databricks Certified-Data-Engineer-Professional Exam
What is the Databricks Certified Data Engineer Professional exam all about?
The Certified-Data-Engineer-Professional exam is the official Databricks exam behind the Databricks Certified Data Engineer Professional certification, validating the skills measured by the Databricks Certified Data Engineer Professional credential. It sits at the Professional level of the Databricks certification program. It also connects to Databricks Certified Data Engineer Associate, so the knowledge you build here carries over to those tracks as well.
How many questions are on the Certified-Data-Engineer-Professional exam, and how much time do I get?
The Certified-Data-Engineer-Professional exam contains 59 (scored); may include unscored items questions to be completed within 120 minutes. Before exam day, divide the available time by the question count to work out a comfortable per-question pace, and mark any item that eats into it so you can return later instead of getting stuck. Timed sessions in the Getcertkey test engines make that pacing automatic — run at least two full-length mock exams under the clock so time pressure never becomes the reason you drop points.
What score do I need to pass the Certified-Data-Engineer-Professional exam, and what does it cost?
The passing score for the Certified-Data-Engineer-Professional exam is Not publicly disclosed; commonly estimated ~70%, and the official registration fee is USD 200 (plus applicable taxes). Retakes are not discounted — every new attempt means paying the full fee again — so it pays to measure yourself before you book. Work through the 250 practice questions on Getcertkey, sit a timed practice test, and schedule your exam only when your scores are consistently comfortable. That simple habit is the cheapest exam strategy there is.
Are there any prerequisites for the Certified-Data-Engineer-Professional exam?
No formal prerequisites; completion of Databricks Certified Data Engineer Associate and 1+ year of hands-on data engineering experience on Databricks are highly recommended Requirements can change when Databricks revises its certification program, so confirm the current eligibility rules on the official exam page before you register.
How do I register for the Certified-Data-Engineer-Professional exam?
You can book the Databricks Certified Data Engineer Professional exam through the official registration channels below:
As for delivery, the exam is offered in the following format: Online proctored or test center proctored. Choose the option that suits you best when you book your seat.
What official training is recommended for the Certified-Data-Engineer-Professional exam?
Databricks recommends the following training resources for the Databricks Certified Data Engineer Professional exam:
Official courses build the foundation; the 250 practice questions from Getcertkey then show you how that knowledge is examined, so the two work best together.
Can I try the Certified-Data-Engineer-Professional practice questions before I buy?
Yes. Getcertkey provides a free Certified-Data-Engineer-Professional PDF demo so you can review the question style and answer quality before purchasing. Every purchase also includes 365 days of free updates — if Databricks revises the exam during that period, the updated material reaches you at no cost. Once the free-update year ends, you can extend your update service at a 50% discount.
What if I fail the Certified-Data-Engineer-Professional exam, and how is my order delivered?
Every Databricks Certified Data Engineer Professional purchase on Getcertkey is covered by a 100% money-back guarantee with clear conditions: if you take the corresponding exam within 60 days of your purchase and do not pass, you can claim a full refund by submitting a scanned copy of your exam enrollment slip and your official score report as a PDF within two days of the exam date; claims are processed within seven days of submission. The guarantee does not apply to exams taken within three days of purchase, to material that was downloaded but never used in an exam attempt, or to free products and expired orders, and the candidate name must match the payer name. If you would rather not take a refund, you can instead exchange your purchase for two free exam preparation products of equal value and keep the update service on your original product.
Delivery is instant: your download is sent to your email within one minute of payment, with no limit on how many computers you may install the material on. If nothing arrives within two hours, check your spam folder and contact customer service for help.
What topics are covered in the Certified-Data-Engineer-Professional exam?
The Databricks Certified Data Engineer Professional exam blueprint is organized into 9 domains. The first three are:
- Streaming Workloads and Change Data Capture — ~11% of the exam
- Monitoring, Logging, and Troubleshooting — ~8% of the exam
- Data Sharing and Federation — ~8% of the exam
For the complete domain-by-domain breakdown, scroll up to the full exam topics outline above and use it to plan how you distribute your study time.
Databricks Certified Data Engineer Professional Sample Questions:
Question 1
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
Question 2
A company processes semi-structured JSON files from an external source using Auto Loader in a classic Databricks job. Occasionally, records arrive with null critical fields, invalid types, or unexpected nested schema variations. The engineer must ensure that malformed or non- conforming records are not dropped silently and are captured in a separate quarantine table. The pipeline should continue processing good records into the Bronze layer without failing the job, and the approach must support both batch and streaming ingestion.
The data engineer needs to build a robust ingestion pattern that automatically routes bad records to a quarantine Delta table, while still ingesting good records into the Bronze layer for further processing.
Which approach fulfills the quarantine mechanism in this ingestion architecture?
A. Use Auto Loader with failFast mode to set to false, and enable schema evolution; invalid records will be silently ignored during ingestion.
B. Use Auto Loader with LDP and implement an EXPECT () constraint with a record audit logic to route bad records.
C. Use Lakeflow Spark Declarative Pipelines with a SQL pipeline; configure it to drop rows with nulls using where critical_fields is not null, and rely on audit logs for malformed data.
D. Create a notebook job with inferSchema=True, write a streaming query with .foreachBatch() and catch exceptions using try/except to redirect failed batches to quarantine.
Question 3
A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?
A. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
B. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only
C. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only
D. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only
Question 4
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")
The data engineer notices the query slowing down significantly.
Which solution would improve the performance?
A. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
B. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
C. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours
D. Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour
Question 5
A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)
A. Enable deletion vectors on the table if not already enabled.
B. Overwrite the table instead of Merge.
C. Partition the table by date.
D. Use ZORDER on high-cardinality columns.
E. Apply liquid clustering using the merge join keys.
Solutions:
| Question 1 Answer: D | Question 2 Answer: B | Question 3 Answer: C | Question 4 Answer: D | Question 5 Answer: A,E |
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