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Databricks Certified Data Engineer Professional Sample Questions:

1. 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 create a fork of the remote repository commit all changes and make a pull request on the source repository
B) Use repos to merge all difference and make a pull request back to the remote repository.
C) Use Repos to merge all differences and make a pull request back to the remote repository.
D) Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
E) Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
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2. A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A) Define and import unit test functions from a separate Databricks notebook
B) Run unit tests against non-production data that closely mirrors production
C) Define and unit test functions using Files in Repos
D) Define units test and functions within the same notebook

3. 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?
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A) A user can only transfer job ownership to a group if they are also a member of that group.
B) Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
C) Only workspace administrators can grant "Owner" privileges to a group.
D) The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
E) Other than the default "admins" group, only individual users can be granted privileges on jobs.

4. The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A) All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
B) In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
C) Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
D) Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.

5. Which statement describes Delta Lake Auto Compaction?

A) Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
B) An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
C) Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
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D) An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
E) Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

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