New Launch Databricks-Certified-Professional-Data-Engineer PDF Dumps [2026] - Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q163-Q168):

NEW QUESTION # 163
A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository.
Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.

A. Use Repos to merge all differences and make a pull request back to the remote repository.
B. Use repos to merge all difference and make a pull request back to the remote repository.
C. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
D. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

Answer: C

Explanation:
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.
References:
* Databricks documentation on using Repos with Git: https://docs.databricks.com/repos.html

NEW QUESTION # 164
You are asked to setup two tasks in a databricks job, the first task runs a notebook to download the data from a remote system, and the second task is a DLT pipeline that can process this data, how do you plan to configure this in Jobs UI

A. Single job cannot have a notebook task and DLT Pipeline task, use two different jobs with linear dependency.
B. Jobs UI does not support DTL pipeline, setup the first task using jobs UI and setup the DLT to run in continuous mode.
C. Add first step in the DLT pipeline and run the DLT pipeline as triggered mode in JOBS UI
D. Jobs UI does not support DTL pipeline, setup the first task using jobs UI and setup the DLT to run in trigger mode.
E. Single job can be used to setup both notebook and DLT pipeline, use two different tasks with linear dependency.

Answer: E

Explanation:
Explanation
The answer is Single job can be used to set up both notebook and DLT pipeline, use two different tasks with linear dependency, Here is the JOB UI
1.Create a notebook task
2.Create DLT task
a.add notebook task as dependency
3.Final view
Create the notebook task
Graphical user interface, text, application, email Description automatically generated

DLT task
Graphical user interface, text, application, email Description automatically generated

Final view
Graphical user interface, text, application, PowerPoint Description automatically generated

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Top of Form

NEW QUESTION # 165
A junior data engineer on your team has implemented the following code block.

The viewnew_eventscontains a batch of records with the same schema as theeventsDelta table.
Theevent_idfield serves as a unique key for this table.
When this query is executed, what will happen with new records that have the sameevent_idas an existing record?

A. They are ignored.
B. They are deleted.
C. They are merged.
D. They are inserted.
E. They are updated.

Answer: A

Explanation:
Explanation
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.

NEW QUESTION # 166
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
B. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
C. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
D. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
E. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.

Answer: C

Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified References: [Databricks Certified Data Engineer Professional], under
"Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.

NEW QUESTION # 167
Which of the following locations in the Databricks product architecture hosts the notebooks and jobs?

A. Control plane
B. Databricks Filesystem
C. Databricks web application
D. Data plane
E. JDBC data source

Answer: A

Explanation:
Explanation
The answer is Control Pane,
Databricks operates most of its services out of a control plane and a data plane, please note serverless features like SQL Endpoint and DLT compute use shared compute in Control pane.
Control Plane: Stored in Databricks Cloud Account
*The control plane includes the backend services that Databricks manages in its own Azure account. Notebook commands and many other workspace configurations are stored in the control plane and encrypted at rest.
Data Plane: Stored in Customer Cloud Account
*The data plane is managed by your Azure account and is where your data resides. This is also where data is processed. You can use Azure Databricks connectors so that your clusters can connect to external data sources outside of your Azure account to ingest data or for storage.
Timeline Description automatically generated

NEW QUESTION # 168
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