Free DP-700 Exam Braindumps
Implementing Data Engineering Solutions Using Microsoft Fabric Questions 46–50 (page: 11)

Updated On: 17-Sep-2026

HOTSPOT (Drag and Drop is not supported) (Drag and Drop is not supported)
You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse1 contains a table named DimCustomers. DimCustomers contains the following columns:
-CustomerName
-CustomerID
-BirthDate
-Email
You need to configure security to meet the following requirements:
-BirthDate in DimCustomer must be masked and display 1900-01-01.
-Email in DimCustomer must be masked and display only the first leading character and the last five characters.
How should you complete the statement? To answer, select the appropriate options in the answer area.
Note: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box 1: random(1900-01-01, 1900-01-01) BirthDate in DimCustomer must be masked and display 1900-01-01.
Random A random masking function for use on any numeric type to mask the original value with a random value within a specified range. Example definition syntax: Account_Number bigint MASKED WITH (FUNCTION = 'random ([start range], [end range])')
Example of alter syntax: ALTER COLUMN [Month] ADD MASKED WITH (FUNCTION = 'random(1, 12)')
Box 2: 'partial(1,"@",5)' EmailAddress in DimCustomer must be masked and display only the first leading character and the last five characters.
Custom String Masking method that exposes the first and last letters and adds a custom padding string in the middle. prefix,[padding],suffix
If the original value is too short to complete the entire mask, part of the prefix or suffix isn't exposed.
xample definition syntax: FirstName varchar(100) MASKED WITH (FUNCTION = 'partial(prefix, [padding],suffix)') NULL
Example of alter syntax: ALTER COLUMN [Phone Number] ADD MASKED WITH (FUNCTION = 'partial (1,"XXXXXXX",0)')
This turns a phone number like 555.123.1234 into 5XXXXXXX.
Incorrect: * 'default()'
Full masking according to the data types of the designated fields. default() displays XXXX.


Reference:

https://learn.microsoft.com/en-us/fabric/data-warehouse/howto-dynamic-data-masking



You have a Fabric workspace named Workspace1 that contains the following items:
-A Microsoft Power BI report named Report1
-A Power BI dashboard named Dashboard1
-A semantic model named Model1
-A lakehouse name Lakehouse1
Your company requires that specific governance processes be implemented for the items.
Which items can you endorse in Fabric?

  1. Lakehouse1, Model1, and Dashboard1 only
  2. Lakehouse1, Model1, Report1 and Dashboard1
  3. Report1 and Dashboard1 only
  4. Model1, Report1, and Dashboard1 only
  5. Lakehouse1, Model1, and Report1 only

Answer(s): E

Explanation:

Not the Power BI dashboard Dashboard1.
Note: Endorsement is a Fabric and Power BI feature that makes it easier for users in an organization to find high-quality, trust-worthy content and data. Endorsed items are clearly labeled in the UI with a badge, and in some lists they are given precedence and are listed first.
Items can receive one of three endorsement badges - Promoted, Certified, or Master data:
Promoted: When an item has the Promoted badge, it means that the item creators think the item is ready for sharing and reuse. Any Fabric or Power BI item except Power BI dashboards can be promoted. Any user with write permissions on an item can promote it.
Certified: When an item has the Certified badge, it means that an organization-authorized reviewer has certified that the item meets the organization's quality standards, can be regarded as reliable and authoritative, and is ready for use across the organization. Any Fabric or Power BI item except Power BI dashboards can be certified. Any user can request certification for an item, but only users specified by a Fabric administrator can actually certify items.


Reference:

https://learn.microsoft.com/en-us/fabric/governance/endorsement-overview



You have a Fabric workspace named Workspace1.
Your company acquires GitHub licenses.
You need to configure source control for Workpace1 to use GitHub. The solution must follow the principle of least privilege.
Which permissions do you require to ensure that you can commit code to GitHub?

  1. Actions (Read and write) and Contents (Read and write)
  2. Actions (Read and write) only
  3. Contents (Read and write) only
  4. Contents (Read) and Commit statuses (Read and write)

Answer(s): C

Explanation:

Microsoft Fabric Git integration
Git roles The following table describes the Git permissions needed to perform various common operations: If you're using a fine-grained access token, the following permissions are needed for common operations:
* Commit workspace changes to Git Contents= Access: Read and write branch policy should allow direct commit
Note: * Commit workspace changes to Git All of the following roles: Contributor in the workspace (WRITE permission on all items) Owner of the item (if the tenant switch blocks updates for nonowners) BUILD on external dependencies (where applicable)


Reference:

https://learn.microsoft.com/en-us/fabric/cicd/git-integration/git-integration-process



You have a Fabric workspace named Workspace1.
You plan to configure Git integration for Workspace1 by using an Azure DevOps Git repository.
An Azure DevOps admin creates the required artifacts to support the integration of Workspace1.
Which details do you require to perform the integration?

  1. the organization, project, Git repository, and branch
  2. the personal access token (PAT) for Git authentication and the Git repository URL
  3. the project, Git repository, branch, and Git folder
  4. the Git repository URL and the Git folder

Answer(s): A

Explanation:

Microsoft Fabric Git integration
Connect to your new Azure DevOps git repository, then click on the Connect and sync button:


Reference:

https://knowledge.bigenius-x.com/microsoft-fabric-stage-files-target-environment



You have a Fabric workspace that contains a lakehouse and a semantic model named Model1.
You use a notebook named Notebook1 to ingest and transform data from an external data source.
You need to execute Notebook1 as part of a data pipeline named Pipeline1. The process must meet the following requirements:
-Run daily at 07:00 AM UTC.
-Attempt to retry Notebook1 twice if the notebook fails.
-After Notebook1 executes successfully, refresh Model1.
Which three actions should you perform? Each correct answer presents part of the solution.
Note: Each correct selection is worth one point.

  1. Place the Semantic model refresh activity after the Notebook activity and link the activities by using the On success condition.
  2. From the Schedule settings of Pipeline1, set the time zone to UTC.
  3. Set the Retry setting of the Notebook activity to 2.
  4. From the Schedule settings of Notebook1, set the time zone to UTC.
  5. Set the Retry setting of the Semantic model refresh activity to 2.
  6. Place the Semantic model refresh activity after the Notebook activity and link the activities by using an On completion condition.

Answer(s): A,B,C

Explanation:

Only execute the Semantic model refresh activity if Notebook1 is successful The pipeline is to be scheduled, not the notebook activity. Attempt to retry Notebook1 twice if the notebook fails.



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