Key Considerations for Data Migration

Correct Answers: A and.

C.

When Solution Architects create a data migration strategy, they need to define the data validation parameters.

These parameters include data quality and data quantity.

Data quality is the accuracy of the data values because of data mapping.

Data quantity is the expected number of rows migrated to the Dataverse.

Data quality and data quantity are essential values for data migration control.

Because of data transformation or changed schema, or some other factors, the number of input rows can differ from the number of rows in a new database.

A Solution Architect needs to work with stakeholders to verify the data quality.

Both these parameters are vital for testing strategy as well.

Your test team needs to know the acceptance criteria for the data migration.

All other options are incorrect.

For more information about data migration strategy, please visit the below URL:

When migrating legacy data to a new Power Platform solution, it's important to have a data migration strategy in place. Here are two items that should be addressed in this strategy:

1. Data quality: Data quality refers to the accuracy, completeness, and consistency of the data being migrated. Poor data quality can lead to issues such as data duplication, incorrect data values, and data inconsistencies, which can cause problems in the new system. Therefore, it's important to assess the quality of the legacy data before the migration and implement data cleansing processes to ensure that the data being migrated is accurate, complete, and consistent.

2. Data loss prevention: Data loss prevention refers to the measures taken to ensure that no data is lost during the migration process. Data loss can occur due to technical issues, human error, or other unforeseen circumstances. Therefore, it's important to have backup and recovery plans in place and to test them before the migration to minimize the risk of data loss. Additionally, it's important to have a rollback plan in case the migration encounters unforeseen issues that require a rollback to the previous state.

While the other options presented in the question (data quantity, data rollup, and data connector) may also be important in a data migration strategy, they are not as critical as data quality and data loss prevention. Data quantity is generally less of a concern because the amount of data being migrated can be controlled through data mapping and filtering. Data rollup and data connector may be relevant if the new Power Platform solution requires data aggregation or integration with other systems, but they are not critical to the success of the data migration itself.