The Databricks Certified Data Engineer Professional exam validates your ability to design, build, and maintain data pipelines on the Databricks platform. This certification is intended for engineers with hands-on experience in data processing, modeling, and governance who want to demonstrate expertise in the Data Engineer Professional path. This page outlines the exam syllabus, question formats, and effective preparation strategies to help you succeed.
Use this topic map to guide your study for Databricks Databricks-Certified-Professional-Data-Engineer (Databricks Certified Data Engineer Professional) within the Data Engineer Professional path.
The exam uses multiple question formats to assess both conceptual knowledge and practical decision-making in real-world scenarios.
Questions progress in difficulty and reflect the complexity of production data engineering work on Databricks.
An effective study plan maps the six core topics to a structured timeline, with regular practice and review to reinforce connections between concepts. Allocate study time proportionally to topic weight and your own knowledge gaps.
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Data Processing and Security and Governance typically account for a larger portion of the exam. However, all six domains are tested, so balanced preparation across Databricks Tooling, Data Modeling, Monitoring and Logging, and Testing and Deployment is essential for a strong score.
In practice, these domains are interdependent. You use Databricks Tooling to build pipelines that apply Data Processing and Data Modeling logic, while Security and Governance controls who can access the data. Monitoring and Logging tracks pipeline health, and Testing and Deployment ensures code quality before production. Understanding these connections helps you answer scenario-based questions more accurately.
The exam is designed for engineers with at least six months of practical experience building data pipelines on Databricks or similar platforms. Hands-on labs focusing on cluster configuration, Delta Lake operations, and job scheduling are especially valuable for reinforcing exam concepts.
Many candidates underestimate Security and Governance topics and focus too heavily on Data Processing. Others miss questions by not reading scenario details carefully or by confusing similar features. Reviewing explanations for practice test errors and revisiting weak topics in the final week helps avoid these pitfalls.
In your last week, take a full-length timed practice test to identify remaining gaps. Spend 60 percent of remaining study time on weak domains and 40 percent reviewing high-confidence areas to maintain retention. Avoid learning new topics; instead, reinforce understanding through targeted practice questions and explanation reviews.
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy.
Which code snippet correctly sets this retention period for deleted files?
In Delta Lake, the property delta.deletedFileRetentionDuration controls how long deleted data files are retained before being permanently removed during a VACUUM operation.
By default, this retention duration is set to 7 days.
To comply with stricter retention requirements, organizations can explicitly update the table property using an ALTER TABLE statement.
Option A uses the correct SQL command:
ALTER TABLE my_table SET TBLPROPERTIES ('delta.deletedFileRetentionDuration' = 'interval 15 days')
This updates the Delta table metadata so that all future operations respect the 15-day retention policy for deleted files.
Why not the others?
B: This code incorrectly tries to set the property via the DeltaTable API. Delta's Python API does not expose direct attributes like deletedFileRetentionDuration; instead, properties must be set through ALTER TABLE or DataFrameWriter options.
C: VACUUM ... RETAIN specifies a one-time file cleanup action (e.g., retaining 15 hours of history), not a persistent retention policy. It cannot be used to set a continuous retention duration.
D: Setting spark.conf applies a session-level configuration and does not permanently update the table's retention metadata. Once the session ends, this configuration is lost.
Therefore, Option A is the correct and documented approach for persistently enforcing a 15-day deleted file retention period in Delta Lake.
A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses.
Which design approach for the date dimension will support efficient time-based querying and aggregation?
In dimensional modeling, Databricks recommends denormalized, attribute-rich dimension tables for performance and usability. A date dimension should include all commonly used derived time attributes such as fiscal period, quarter, month, weekday, and holiday flags. Precomputing these attributes ensures consistent business logic, eliminates repeated calculations during query time, and enables efficient filtering and aggregation. The documentation for Delta Lake and Lakehouse design explicitly advises precomputing these attributes for analytical workloads that depend heavily on time-based slicing. Options A and C degrade performance and consistency, while maintaining multiple calendar-specific dimension tables (B) complicates the model unnecessarily.
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.
Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Language Interoperability'' section; Databricks Documentation, under ''Mix languages'' section.
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?
The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl dbfs:/mnt/mylib/mylib-0.1-py3-none-any.whl
This will upload the custom Python Wheel to the cluster and make it available for use with a production job. You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.
Libraries CLI (legacy): https://docs.databricks.com/en/archive/dev-tools/cli/libraries-cli.html
Library operations: https://docs.databricks.com/en/dev-tools/cli/commands.html#library-operations
Install or update the Databricks CLI: https://docs.databricks.com/en/dev-tools/cli/install.html
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?
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Delta Lake'' section; Databricks Documentation, under ''DROP DUPLICATES'' section.
