Free Access to Microsoft.MB-280.v2026-10-03.q144 with Valid Practice Test (Page 28)

Question 131

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
A company's IT department has a .CSV file stored on one of their Shared Documents folders within their Microsoft SharePoint sites. The data from the .CSV file is ingested into Dynamics 365 Customer Insights - Data.
The file contains a row header and columns of different types, such as quantities and prices. The file also contains some rows with a high proportion of nulls.
You need to clean and transform the data in Customer Insights - Data to be ready for unification.
Solution: Define column types to be appropriate field types and name the query. Create a full name and full address columns by merging the appropriate columns, if they exist. Select Next and your data is now ready for unification.
Does this meet the goal?

A.Yes
B.No

Correct Answer: B

Correct:
* Transform the first row to be used as headers. Define column types to be the appropriate field types and name the query. Create a full name and full address columns by merging the appropriate columns if they exist. Select Next and your data is now ready for unification.
The proposed solution effectively prepares the data for unification in Dynamics 365 Customer Insights - Data. Here's how each step contributes to meeting the goal:
Transform the first row to be used as headers: This step is necessary to define the column names, which is critical for accurate data interpretation.
Define column types to be the appropriate field types: Specifying the correct data types for each column ensures that the data will be processed correctly during unification, maintaining data integrity.
Create a full name and full address columns by merging the appropriate columns if they exist: This step enhances the dataset by consolidating relevant information into single columns, which can simplify data usage and improve data quality. Merging columns helps ensure that users can easily access essential information without navigating through multiple fields.
Select Next: This indicates that the data transformation steps are completed and the dataset is ready for the unification process.
Incorrect:
* Define column types to be appropriate field types and name the query. Create a full name and full address columns by merging the appropriate columns, if they exist. Select Next and your data is now ready for unification.
Does not address the problem with null values.
* Remove any rows where the primary key is missing, delete any leading or trailing zeros on the primary key, and name the query. Select Next and your data is now ready for unification.
Problem not related to the primary key.
* Transform the first row to be used as headers, and remove any special characters or spaces from header row. Remove rows with missing primary keys and name the query. Select Next and your data is now ready for unification.
Does not address the problem with null values.
* Transform the first row to be used as headers, define column types to be the appropriate field types and name the query. Select Next and your data is now ready for unification.
Solution removes all rows with null values, which can lead to significant data loss, especially if those rows contain important information.
It may compromise data quality by eliminating rows, which can impact analysis and insights.
* Transform the first row to be used as headers, remove rows that contain null values, and name the query. Select Next and your data is now ready for unification.
While the solution includes transforming the first row to be used as headers and naming the query, the step of removing rows that contain null values is problematic.
Removing all rows with null values can lead to significant data loss, particularly if those rows contain relevant information.

Question 132

Hotspot Question
A Company uses Exchange Online. Users require their appointments and tasks to automatically synchronize with those available in Outlook.
You need to set up the default mailbox configuration.
What should you select for each field? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 133

Hotspot Question
A company sells telephones. The company has a list of telephone colors that customers can choose.
For one month, the company wants to sell a red phone at a special price.
You need to set up the red phone for the sales team.
How should you configure the product and price list items? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 134

A sales manager wants to implement an automated system to assign leads to the appropriate salesperson based on the region. Which features should be configured to achieve this? (Select threE.

A.Assignment Rules
B.Queues
C.Custom Dashboards
D.Auto-Numbering
E.Routing Rules

Question 135

You are a Dynamics 365 Sales administrator. You configure a forecast template that uses the Forecast category as a starting point for a layout for the sales team.
The sales manager wants the value of the Lost column to come from the Total Detail Amount instead of the default value because the revenue is always driven by the items.
You need to make the change.
What should you do?

A.Edit the existing forecast and update the Amount column in the layout.
B.Edit the existing forecast, remove the Lost column, and add a new calculated column.
C.Create a new forecast and update the Amount column in the layout.
D.Create a new forecast, remove the Lost column, and add a new calculated column.

Question 131

Question 132

Question 133

Question 134

Question 135

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