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Databricks Databricks-Certified-Professional-Data-Engineer exam : Databricks Certified Professional Data Engineer Exam
- Exam Code: Databricks-Certified-Professional-Data-Engineer
- Exam Name: Databricks Certified Professional Data Engineer Exam
- Updated: Oct 14, 2026
- Q & A: 217 Questions and Answers
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About Databricks Databricks-Certified-Professional-Data-Engineer Exam Simulator
Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
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Reference: https://www.databricks.com/learn/certification/data-engineer-professional
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Databricks Certified Professional Data Engineer Sample Questions:
Question #1
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:
Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?
- A. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
- B. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
- C. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
- D. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
- E. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
Correct Answer: D 🗳️
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Question #2
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?
- A. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
- B. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
- C. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
- D. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
Correct Answer: B 🗳️
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Question #3
A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails.
Which two approaches will meet these requirements? (Choose 2 answers)
- A. Create a single orchestrator notebook that calls each step with dbutils.notebook.run(), defining a job for that notebook and configuring retries and notifications at the notebook level.
- B. Use Databricks Asset Bundles (DABs) to deploy the workflow, then trigger individual tasks directly by referencing each task's notebook or script path in the workspace.
- C. Create a multi-task job using the UI, Databricks Asset Bundles (DABs), or the Jobs REST API (/jobs
/create) with notebook, Python wheel, and SQL tasks. Configure task-level retries and email notifications in the job definition. - D. Trigger the job programmatically using the Databricks Jobs REST API (/jobs/run-now), the CLI (databricks jobs run-now), or one of the Databricks SDKs.
- E. Use the REST API endpoint /jobs/runs/submit to trigger each task individually as separate job runs and implement retries using custom logic in the orchestrator.
Correct Answer: C,D 🗳️
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Question #4
A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?
- A. SET TAGS FOR table_name AS ( ' key1 ' = ' value1 ' , ' key2 ' = ' value2 ' );
- B. COMMENT ON TABLE table_name TAGS ( ' key1 ' = ' value1 ' , ' key2 ' = ' value2 ' );
- C. APPLY TAGS ON table_name VALUES ( ' key1 ' = ' value1 ' , ' key2 ' = ' value2 ' );
- D. ALTER TABLE table_name SET TAGS ( ' key1 ' = ' value1 ' , ' key2 ' = ' value2 ' );
Correct Answer: D 🗳️
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Question #5
Which distribution does Databricks support for installing custom Python code packages?
- A. nom
- B. sbt
- C. CRAM
- D. CRAN
- E. Wheels
- F. jars
Correct Answer: A 🗳️
Viewing page 1 out of 44 pages.
Viewing questions 1-5 out of 217 questions.
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