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Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER  Exam Questions & Answers
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Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Exam Questions & Answers


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  • Vendor: Databricks

    Exam Code: DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER

    Exam Name: Databricks Certified Data Engineer Professional

    Certification Provider: Databricks

    Total Questions: 127 Q&A ( View Details)

    Updated on: Jul 07, 2026

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DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER Online Practice Questions and Answers

Questions 1

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.

Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.

B. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.

C. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.

D. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.

E. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.

Show Answer

Correct Answer: D

In Databricks notebooks, using the display() function triggers an action that forces Spark to execute the code and produce a result. However, Spark operations are generally divided into transformations and actions. Transformations create a

new dataset from an existing one and are lazy, meaning they are not computed immediately but added to a logical plan. Actions, like display(), trigger the execution of this logical plan. Repeatedly running the same code cell can lead to

misleading performance measurements due to caching. When a dataset is used multiple times, Spark's optimization mechanism caches it in memory, making subsequent executions faster. This behavior does not accurately represent the

first-time execution performance in a production environment where data might not be cached yet.

To get a more realistic measure of performance, it is recommended to:

Clear the cache or restart the cluster to avoid the effects of caching. Test the entire workflow end-to-end rather than cell-by-cell to understand the cumulative performance.

Consider using a representative sample of the production data, ensuring it includes various cases the code will encounter in production.

References:

Databricks Documentation on Performance Optimization: Databricks Performance Tuning

Apache Spark Documentation: RDD Programming Guide-Understanding transformations and actions

Questions 2

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.

Show Answer

Correct Answer: E

The reason why the junior data engineer cannot transfer "Owner" privileges to the "DevOps" group is that Databricks jobs must have exactly one owner, and the owner must be an individual user, not a group. A job cannot have more than one owner, and a job cannot have a group as an owner. The owner of a job is the user who created the job, or the user who was assigned the ownership by another user. The owner of a job has the highest level of permission on the job, and can grant or revoke permissions to other users or groups. However, the owner cannot transfer the ownership to a group, only to another user. Therefore, the junior data engineer's attempt to transfer "Owner" privileges to the "DevOps" group is not possible. References: Jobs access control: https://docs.databricks.com/security/access-control/table-acls/index.html Job permissions: https://docs.databricks.com/security/access-control/table-acls/privileges.html#job-permissions

Questions 3

The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes. Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.

B. Schedule a Structured Streaming job with a trigger interval of 60 minutes.

C. Schedule a job to execute the pipeline once hour on a new job cluster.

D. Configure a job that executes every time new data lands in a given directory.

Show Answer More Questions

Correct Answer: C

Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and

terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the

cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.

References:

Databricks documentation on jobs and job clusters:

https://docs.databricks.com/jobs.html

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