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  • Exam Name: Databricks Certified Data Engineer Professional Exam
  • Last Update: 15-Oct-2025
  • Questions and Answers: 195
  • Single Choice: 192 Q&A's
  • Multiple Choice: 3 Q&A's

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Databricks-Certified-Professional-Data-Engineer Questions and Answers

Question # 1

An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.

For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.

Which solution meets these requirements?

A.

Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.

B.

Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.

C.

Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.

D.

Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.

E.

Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.

Question # 2

The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Databricks-Certified-Professional-Data-Engineer question answer

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A.

Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.

B.

No; the Delta cache may return records from previous versions of the table until the cluster is restarted.

C.

Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.

D.

No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.

E.

No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

Question # 3

Which statement describes the correct use of pyspark.sql.functions.broadcast?

A.

It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.

B.

It marks a column as small enough to store in memory on all executors, allowing a broadcast join.

C.

It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.

D.

It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.

E.

It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Dumps FAQs

The Databricks Certified Professional Data Engineer exam assesses your ability to design, develop, and deploy production-ready data processing pipelines using the Databricks platform. It validates your expertise in working with Apache Spark, Delta Lake, MLflow, and other tools within the Databricks ecosystem.

There are several benefits to becoming a Databricks Certified Professional Data Engineer, including:

  • Increased knowledge and credibility in using Databricks for complex data engineering tasks.
  • Enhanced job prospects in big data and data engineering roles seeking Databricks expertise.
  • Demonstrated ability to build and manage scalable data pipelines on the Databricks platform.
  • Stronger foundation for pursuing specialized Databricks certifications in specific areas.

There are no formal prerequisites for the exam. However, Databricks recommends having at least one year of hands-on experience performing data engineering tasks on the Databricks platform.

The exam covers a broad range of Databricks data engineering functionalities, including:

  • Building and optimizing data processing pipelines with Apache Spark
  • Working with Delta Lake for data storage, management, and version control
  • Utilizing MLflow for machine learning model lifecycle management
  • Deploying and monitoring data pipelines in production environments
  • Designing and implementing data quality checks and data governance practices
  • Utilizing Databricks SQL and other tools for data exploration and analysis

The exam is a proctored, online assessment consisting of multiple-choice questions. You will not be given access to external resources during the exam.

The cost of the Databricks Professional Data Engineer exam is currently $395 USD.

The Databricks Professional Data Engineer certification does not have an expiration date. However, the Databricks platform and data engineering best practices are constantly evolving, so staying updated is recommended.

The Databricks Associate Developer certification focuses on basic Databricks functionalities for data manipulation and analysis. The Professional Data Engineer certification goes deeper, requiring expertise in building and managing complex data pipelines for production environments.

While not mandatory, a strong understanding of Apache Spark concepts and functionalities is crucial for success on the exam, as Spark is a core technology used within the Databricks platform for data processing.

Databricks does not publicly disclose the passing score. However, it is generally considered to be around 70% or higher.

If you fail the exam, you will receive a notification with your score report. Analyze the report to identify areas needing improvement. You can retake the exam after a waiting period (typically 30 days) after focusing on strengthening

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