Databricks Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional

  • Exam Code: Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional
  • Updated: Oct 11, 2026
  • Q & A: 250 Questions and Answers

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About Databricks Certified-Data-Engineer-Professional Exam Questions

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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

SectionWeightObjectives
Data Modeling~10%- Design scalable Delta Lake schemas and clustering
- Apply dimensional modeling techniques
Monitoring, Logging, and Troubleshooting~8%- Diagnose common pipeline and job failures
- Use Spark UI, Query Profiler, and system tables
CI/CD, Testing, and Deployment~6%- Deploy with Declarative Automation Bundles, CLI, and REST API
- Implement testing and deployment pipelines
Cost and Performance Optimization~13%- Leverage system tables and observability tools
- Optimize queries, clusters, and storage
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
Streaming Workloads and Change Data Capture~11%- Apply AUTO CDC APIs and exactly-once semantics
- Implement reliable streaming pipelines
Data Sharing and Federation~8%- Configure Delta Sharing and Lakehouse Federation
Data Transformation, Cleansing, and Quality~12%- Apply advanced Spark transformations
- Enforce data quality and quarantine bad data
Security and Governance~10%- Manage Unity Catalog permissions and ACLs
- Implement row-level security, column masking, and compliance

Your Databricks Certified Data Engineer Professional Questions, Answered

Registration for the Databricks Certified Data Engineer Professional goes through the official channels listed here:

As for the format, the Certified-Data-Engineer-Professional exam is delivered Online proctored or test center proctored — pick whichever fits your situation when you schedule.

The official Databricks Certified Data Engineer Professional blueprint is divided into 9 domains, led by Data Modeling (~10%), Monitoring, Logging, and Troubleshooting (~8%), and Streaming Workloads and Change Data Capture (~11%). The full domain-by-domain breakdown is in the syllabus section above — that is the checklist your study plan should follow.

The passing score for the Certified-Data-Engineer-Professional exam is Not publicly disclosed; commonly estimated ~70%, and registering officially costs USD 200 (plus applicable taxes). That fee buys exactly one attempt — if you fall short, the retake is charged in full again, no discount for a second try. The practical move is to measure yourself first: drill the 250 practice questions at BraindumpsPrep under timed conditions until your mock scores clear the bar with room to spare, then book your seat.

You can. BraindumpsPrep publishes a free PDF demo of the Certified-Data-Engineer-Professional practice questions — download it, work through it, and decide on evidence rather than promises. Once you own the full product, 365 days of free updates are included, and an expired product can have its update service renewed at a 50% discount from your member zone.

The Certified-Data-Engineer-Professional exam — officially the Databricks Certified Data Engineer Professional — is how Databricks verifies that you can apply its technologies in real working scenarios, and passing it earns you the Databricks Certified Data Engineer Professional certification, a credential at the Professional level. Employers recognize it because it is vendor-issued and skills-based. Candidates often continue toward related credentials such as Databricks Certified Data Engineer Associate once this one is in hand.

Yes — Databricks lists these training options for the Databricks Certified Data Engineer Professional:

Courseware teaches the concepts; repetition makes them exam-proof. Combine whichever course you choose with the 250 practice questions from BraindumpsPrep and you cover both halves of preparation.

You will face 59 (scored); may include unscored items questions within 120 minutes on the Certified-Data-Engineer-Professional exam. Do the math once and the lesson is clear: dwell too long on a hard item and the clock eats your easy points later. The fix is rehearsal, not luck — take full-length timed mocks in the BraindumpsPrep desktop or online test engine until pacing becomes automatic, and learn to flag a question, move on, and return with fresh eyes.

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No formal prerequisites; completion of Databricks Certified Data Engineer Associate and 1+ year of hands-on data engineering experience on Databricks are highly recommended

Vendor policies shift from time to time, so treat this as your starting point and verify the current criteria on the official Databricks exam page before you schedule anything.

Databricks Certified Data Engineer Professional Sample Questions:

Question #1

A data team is working to optimize an existing large, fast-growing table 'orders' with high cardinality columns, which experiences significant data skew and requires frequent concurrent writes. The team notice that the columns 'user_id', 'event_timestamp' and 'product_id' are heavily used in analytical queries and filters, although those keys may be subject to change in the future due to different business requirements. Which partitioning strategy should the team choose to optimize the table for immediate data skipping, incremental management over time, and flexibility?

  • A. Cluster the table with: ALTER TABLE orders CLUSTER BY user_id, product_id, event_timestamp
  • B. Partition the table with: ALTER TABLE orders PARTITION BY user_id, product_id, event_timestamp
  • C. Z-order the table with OPTIMIZE orders ZORDER BY (user_id, product_id, event_timestamp)
  • D. Use z-order after partitiing the table: OPTIMIZE orders ZORDER BY (user_id, product_id) WHERE event_timestamp = current date () - 1 DAY
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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Question #2

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 job_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 run_id and include_history parameters
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #3

The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

  • A. DESCRIBE EXTENDED dev.pii test
  • B. DESCRIBE HISTORY dev.pii test
  • C. SHOW TBLPROPERTIES dev.pii test
  • D. DESCRIBE DETAIL dev.pii test
  • E. SHOW TABLES dev
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Question #4

A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

  • A. Task queueing resulting from improper thread pool assignment.
  • B. Skew caused by more data being assigned to a subset of spark-partitions.
  • C. Network latency due to some cluster nodes being in different regions from the source data
  • D. Spill resulting from attached volume storage being too small.
  • E. Credential validation errors while pulling data from an external system.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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Question #5

A data engineer is evaluating tools to build a production-grade data pipeline. The team must process change data from cloud object storage, filter out or isolate invalid records, and ensure the timely delivery of clean data to downstream consumers. The team is small, under tight deadlines, and wants to minimize operational overhead while keeping pipelines auditable and maintainable.
Which approach should the data engineer implement?

  • A. Implement ingestion using Auto Loader with Structured Streaming, and manage invalid data handling and table updates using checkpointing and merge logic.
  • B. Use a hybrid approach: Ingest with Auto Loader into Bronze tables, then process using SQL queries in Databricks Workflows to generate cleaned Silver and Gold tables on a schedule.
  • C. Use LDP to build declarative pipelines with Streaming Tables and Materialized Views, leveraging built-in support for data expectations and incremental processing.
  • D. Ingest data directly into Delta tables via Spark jobs, apply data quality filters using UDFs, and use LDP for creating Materialized Views.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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