Databricks Databricks Certified Data Engineer Professional Practice Exams
Last updated on Oct 23,2026- Exam Code: Databricks Certified Data Engineer Professional
- Exam Name: Databricks Certified Data Engineer Professional Exam
- Certification Provider: Databricks
- Latest update: Oct 23,2026
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI. Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job. They attempt to transfer "Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?
- A . Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
- B . The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
- C . Other than the default "admins" group, only individual users can be granted privileges on jobs.
- D . A user can only transfer job ownership to a group if they are also a member of that group.
- E . Only workspace administrators can grant "Owner" privileges to a group.
A data architect has heard about lake’s built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.
Which piece of information is critical to this decision?
- A . Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
- B . Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
- C . Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
- D . Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires Setting multiple fields in a single update.
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.
- A . "Can Manage" privileges on the required cluster
- B . Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
- C . Cluster creation allowed. "Can Attach To" privileges on the required cluster
- D . "Can Restart" privileges on the required cluster
- E . Cluster creation allowed. "Can Restart" privileges on the required cluster
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?
- A . Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
- B . Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
- C . Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
- D . Create a new table with the required schema and use Delta Lake’s DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
The data science team has requested assistance in accelerating queries on free form text from user reviews.
The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?
- A . Delta Lake statistics are not optimized for free text fields with high cardinality.
- B . Text data cannot be stored with Delta Lake.
- C . ZORDER ON review will need to be run to see performance gains.
- D . The Delta log creates a term matrix for free text fields to support selective filtering.
- E . Delta Lake statistics are only collected on the first 4 columns in a table.
An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:
After the database was successfully created and permissions configured, a member of the finance team runs the following code:
If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?
- A . A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
- B . An external table will be created in the storage container mounted to /mnt/finance eda bucket.
- C . A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
- D . An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
- E . A managed table will be created in the DBFS root storage container.