PDF Version DemoNever touched Databricks technology before? PassExamDumps's Databricks Certified Data Engineer Professional material rewrites the textbook into plain language — Databricks-Certified-Data-Engineer-Professional practice questions explained through examples that even complete industry rookies grasp on the first read.
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 |
| Available Languages: | Korean, Japanese, English, Portuguese (Brazil) |
| Exam Price: | USD 200 |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Exam Duration: | 120 minutes |
| Exam Format: | Multiple choice |
| Passing Score: | Not publicly disclosed |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 59-60 |
| Recommended Training: | Advanced Data Engineering with Databricks Databricks Streaming and Lakeflow Spark Declarative Pipelines |
| Exam Registration: | Databricks Certification Exam Registration |
| Sample Questions: |  |
| Exam Way: | Online proctored or in-person test center |
| Pre Condition: | No mandatory prerequisites; 1+ years hands-on data engineering experience and related training highly recommended |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-professional |
Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access |
| Cost & Performance Optimisation | 13% | - Optimize compute and storage resources - Apply cost management best practices - Improve query and pipeline performance |
| Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Implement complex data processing logic - Use Databricks-specific libraries and APIs |
| Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs |
| Data Modelling | 6% | - Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture |
| Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Implement access control and permissions - Ensure data privacy and compliance |
| Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming |
| Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards |
| Monitoring and Alerting | 10% | - Track data lineage and metrics - Monitor pipeline performance and health - Set up alerts and notifications |
Databricks Certified Data Engineer Professional Exam: Your Questions, Our Answers
The Databricks-Certified-Data-Engineer-Professional exam is Databricks's official assessment for the Databricks Certified Data Engineer Professional certification, which sits at the Professional level. It verifies that you can work with the Databricks Certified Data Engineer Professional skill set the way real jobs demand — not merely recognize the terminology. It also belongs to a certification family that includes Databricks Certified Data Engineer Associate, so passing it strengthens your position across multiple career paths. For newcomers and veterans alike, the Databricks-Certified-Data-Engineer-Professional exam is the kind of credential that converts effort into evidence.
The Databricks Certified Data Engineer Professional blueprint contains 10 domains, beginning with Data Sharing and Federation (5%), Ensuring Data Security and Compliance (10%), and Developing Code for Data Processing using Python and SQL (22%). Use the weightings like a map of where the points live — the heaviest domains earn the largest share of your study hours. The complete topic list is in the exam topics section above; study against it, and nothing in the exam will feel unannounced.
You're looking at 59-60 questions inside 120 minutes on the Databricks Certified Data Engineer Professional exam. Do the division now and memorize your per-question budget; on exam day, anything that blows its budget gets flagged and revisited with leftover time. Timed practice is what makes this instinctive — the PassExamDumps PC test engine runs time-limited mock exams and scores them automatically, so you arrive already knowing your pace.
Databricks sets the Databricks Certified Data Engineer Professional passing score at Not publicly disclosed, and each attempt costs USD 200 — full price every time, retakes included. That makes preparation the cheaper strategy by a wide margin. The practical path: take auto-scored timed mocks with PassExamDumps practice questions until you clear the passing mark comfortably across multiple runs, then register with confidence grounded in data.
The official Databricks Certified Data Engineer Professional format list includes: Multiple choice. Each format rewards a specific skill — scenario items reward careful reading order, multi-select items reward completeness — so practice should cover all of them, not just your favorites. PassExamDumps's 250 practice questions span the full list, which means the exam's mechanics feel rehearsed and only its content requires thought.
Databricks offers the Databricks-Certified-Data-Engineer-Professional exam in English, Japanese, Portuguese (Brazil), Korean. Choose the language you process fastest when the clock is running — comprehension speed quietly decides borderline results. If English is your pick, working through PassExamDumps's English Databricks-Certified-Data-Engineer-Professional practice questions builds precisely the vocabulary the exam will use against you.
Passing the Databricks Certified Data Engineer Professional exam earns a credential valid for 2 years. Record the expiry date as soon as you pass, and look into recertification options well in advance — a renewal planned early is trivial, a lapsed credential is not. Recertification policy belongs to Databricks and changes periodically, so confirm the current rules on the official certification page.
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Databricks Certified Data Engineer Professional Sample Questions:
A data engineer needs to implement column masking for a sensitive column in a Unity Catalog- managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments. Which approach should the engineer use to efficiently enforce this requirement?
- A. Apply a column mask that references the group_access mapping table in its UDF.
- B. Create a UDF that hardcodes allowed groups and apply it as a column mask.
- C. Use a row filter to restrict access based on the user's group.
- D. Create a view without selecting the sensitive column.
Correct Answer: A 🗳️
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A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?
- A. At some point, the upstream data provider switched everything to multi-line JSON.
- B. The badRecordsPath location is accumulating many small files.
- C. The source data is valid JSON but does not conform to the defined schema in some way.
- D. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
Correct Answer: C 🗳️
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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 to reduce duration of MERGE operations
- B. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
- C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
- D. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
Correct Answer: A 🗳️
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A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?
- A. New data is written to a staging area and clustered during scheduled maintenance.
- B. New data is rejected if it doesn't match the clustering pattern.
- C. New data remains unclustered until the next OPTIMIZE operation.
- D. New data is automatically clustered during write operations.
Correct Answer: C 🗳️
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A data architect is implementing Delta Sharing as part of their data governance strategy to enable secure data collaboration with external partners and internal business units. The architect must establish a permission framework that allows designated data stewards to create shares for their respective domains while maintaining security boundaries and audit compliance. Which specific permissions and roles must be assigned to enable users to create, configure, and manage Delta Shares while maintaining proper security governance and access controls?
- A. Only workspace admins can create and manage shares
- B. Any user with USE_CATALOG privilege can create shares
- C. Users need to be metastore admins or have CREATE SHARE privilege for the metastore
- D. Users need the MANAGE SHARES permission on the workspace
Correct Answer: C 🗳️
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