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Databricks Data Engineer Associate exam dumps: questions 201 to 300 of 531

Page 3 of the free Databricks Data Engineer Associate question bank for the Databricks Certified Data Engineer Associate exam. Questions 201 to 300 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated January 2025

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Free Databricks Data Engineer Associate practice questions

Questions 201 to 205 of 531

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

Databricks Data Engineer Associate Question 201

Single answer

You are working on a Databricks notebook and need to use a custom Python function named transform_data. The function is defined in another file located in a shared workspace repository under the folder path /Workspace/Repos/shared-project/functions/. How would you correctly identify the location of this function and make it usable in your current notebook?

A
Use %run /Workspace/Repos/shared-project/functions/transform_data to import the function.
B
Use import /Workspace/Repos/shared-project/functions/transform_data to import the function.
C
Use %run /Workspace/Repos/shared-project/functions to import all functions in the folder, including transform_data.
D
Use from /Workspace/Repos/shared-project/functions import transform_data to import the function.

Show answer and explanation

Correct answer: A

Explanation

In Databricks, the %run command is used to execute code from another notebook or file so that its contents, including functions, variables, and classes, are accessible in the current notebook. To make the transform_data function usable, you need to specify the exact file path using %run, such as %run /Workspace/Repos/shared-project/functions/transform_data. Other options like import or from ... import are not compatible with workspace paths in Databricks.

A. Correct.
This is the correct way to identify and execute a function defined in another file located in the Databricks workspace. %run executes all the code in the referenced file, making the transform_data function accessible in your notebook.
B. Incorrect.
import cannot be directly used with workspace paths as it is not a valid method for identifying and importing functions in Databricks.
C. Incorrect.
%run with only the folder path will not import the transform_data function specifically. Instead, you need to specify the exact file path to successfully execute the function.
D. Incorrect.
from with the workspace path is not a valid way to import functions from files in Databricks, as it does not recognize workspace paths using this syntax.

Databricks Data Engineer Associate Question 202

Select 4

You are working in Databricks and need to identify the location of a user-defined function (UDF) named calculate_discount that is being used in your notebook. Which of the following approaches can help you locate the function definition?

A
Use the search bar in the Databricks workspace to search for the function name calculate_discount.
B
Check the imports at the beginning of the notebook to see if the function is being imported from an external library or file.
C
Examine all cells in the current notebook to see if the function is defined locally.
D
Inspect the Databricks Repos that the notebook is linked to, as the function might be defined in a version-controlled file.
E
Run the DESCRIBE FUNCTION calculate_discount command in a SQL cell within the notebook.

Show answer and explanation

Correct answers: A, B, C, D

Explanation

To locate the definition of a UDF like calculate_discount, you should consider all possible places where the function might reside. This includes searching the Databricks workspace, checking notebook imports, examining local notebook cells, and inspecting linked Repos. However, SQL commands such as DESCRIBE FUNCTION are only applicable to SQL functions and cannot locate Python UDFs.

A. Correct.
The search bar in the Databricks workspace can help locate resources like notebooks, files, or functions containing the keyword, making it a valid way to locate the function.
B. Correct.
Checking the imports is a necessary step to see if the function is being sourced from an external library or another file, which can guide you to its location.
C. Correct.
Functions can often be defined locally within the cells of the same notebook, so examining the cells is a valid approach to identify its location.
D. Correct.
If the notebook is linked to a Databricks Repo, the function might reside in a version-controlled file within the Repo, making it a valid place to look.
E. Incorrect.
The DESCRIBE FUNCTION command is used for built-in or registered SQL functions, not for Python user-defined functions (UDFs), so it is not applicable in this case.

Databricks Data Engineer Associate Question 203

Single answer

A data engineering team is using Databricks to create and manage SQL User-Defined Functions (UDFs). They want to ensure the security and proper sharing of these UDFs within their organization. Which of the following statements about the security model for sharing SQL UDFs in Databricks is correct?

A
SQL UDFs are accessible only to the creator and cannot be shared with other users.
B
SQL UDFs can be shared with other users in the same workspace by assigning appropriate permissions to the function.
C
SQL UDFs are automatically available to all users in the workspace without requiring additional permissions.
D
SQL UDFs can be shared with users across workspaces as long as the users have the appropriate cluster permissions.

Show answer and explanation

Correct answer: B

Explanation

Databricks implements a security model for SQL UDFs that ensures they are only accessible to users with the appropriate permissions. By sharing UDFs within the same workspace through proper permission assignments, organizations can maintain secure and controlled access to these functions. UDFs are not automatically accessible to all users, nor can they be shared across workspaces.

A. Incorrect.
Incorrect: SQL UDFs can be shared with other users in the workspace by configuring appropriate permissions. They are not restricted to the creator only.
B. Correct.
Correct: SQL UDFs in Databricks can be shared within the same workspace by assigning the required permissions to the UDF. This ensures security and controlled access.
C. Incorrect.
Incorrect: SQL UDFs are not automatically available to all users in the workspace. Permissions need to be explicitly configured to allow sharing.
D. Incorrect.
Incorrect: SQL UDFs cannot be shared across workspaces. They are limited to the workspace where they are created, and permissions must be managed within that workspace.

Databricks Data Engineer Associate Question 204

Single answer

A data engineering team in your organization has created a SQL User-Defined Function (UDF) to standardize address formatting across datasets. They want to share this UDF with another team working on a different workspace within the same Databricks account. Based on Databricks' security model for sharing SQL UDFs, how should this be achieved?

A
By granting access to the UDF's schema to the target team.
B
By exporting the UDF as a JAR file and importing it into the target workspace.
C
By using Unity Catalog to define the UDF and assigning appropriate permissions.
D
By copying the SQL code of the UDF and manually recreating it in the target workspace.

Show answer and explanation

Correct answer: C

Explanation

In Databricks, SQL UDFs can be effectively shared across workspaces using Unity Catalog. Unity Catalog allows you to define UDFs centrally and manage their access through permissions. This ensures secure and consistent sharing of UDFs while adhering to governance policies, making it the recommended approach.

A. Incorrect.
Granting access to the UDF's schema will not enable sharing of the UDF across workspaces; schemas are restricted to a single workspace.
B. Incorrect.
While exporting the UDF as a JAR file could work for Python or Scala UDFs, SQL UDFs are defined and managed differently in Databricks and cannot be shared this way.
C. Correct.
Unity Catalog provides a centralized governance model that supports sharing SQL UDFs across workspaces within the same account by defining them in a catalog and controlling access.
D. Incorrect.
Manually copying and recreating the UDF in another workspace is not a recommended approach, as it introduces the risk of inconsistencies and is not aligned with Databricks' security and governance best practices.

Databricks Data Engineer Associate Question 205

Select 3

You are tasked with creating a SQL User-Defined Function (UDF) in Databricks that needs to be shared across multiple users in your workspace. Which of the following statements correctly describe the security model for sharing SQL UDFs in Databricks?

A
SQL UDFs are stored in the Unity Catalog and follow the same permission model as other Unity Catalog objects.
B
Permissions for SQL UDFs can be granted at the function level to control access.
C
SQL UDFs are automatically accessible to all workspace users once created, without requiring any additional permissions.
D
To use a shared SQL UDF, a user must have the appropriate permissions on both the UDF and the underlying objects it references.
E
SQL UDFs created in a database can only be accessed by the creator and cannot be shared with others.

Show answer and explanation

Correct answers: A, B, D

Explanation

The security model for SQL UDFs in Databricks is tightly integrated with the Unity Catalog. This model ensures that UDFs are governed by the same fine-grained access controls as other Unity Catalog objects. Permissions can be granted at the UDF level and must also align with the permissions on any underlying objects the UDF references. This ensures proper governance and security when sharing UDFs across users.

A. Correct.
Correct: SQL UDFs in Databricks are managed within the Unity Catalog, and their access is governed by the permission model of Unity Catalog, which includes fine-grained access control.
B. Correct.
Correct: Permissions for SQL UDFs can be explicitly granted at the function level, allowing administrators to control who can execute or modify the UDF.
C. Incorrect.
Incorrect: SQL UDFs are not automatically accessible to all users. Access must be granted explicitly through permissions.
D. Correct.
Correct: Users must have the appropriate permissions on both the UDF itself and any underlying objects (e.g., tables) that the UDF references to ensure proper security.
E. Incorrect.
Incorrect: SQL UDFs can be shared with others if permissions are granted. They are not restricted to access by only the creator.

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Databricks Data Engineer Associate practice questions 201 to 300 of 531

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