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.