You have a Power BI semantic model that contains the following data. You need to create a solution that meets the following requirements: - Presents transaction amount totals for each month for the…

Creating one-to-many relationships between dimension tables (like Date and Account) and the fact table (Transactions) is fundamental to a star schema, enabling efficient filtering and aggregation for the required analysis.

In the Transactions query, deleting the TransactionID column minimizes data model size because it is a unique identifier typically not used for analysis or aggregation, thus reducing the number of high-cardinality columns in the fact table.

In the Date query, grouping by MonthNumber would simplify the date dimension but would lose specific date information (like year), which is needed to present data for the current and prior year, and it's not a standard practice for creating a robust date dimension.

In the Transactions query, grouping by AccountID and MonthStartDate for TransactionDate and summing the Amount effectively pre-aggregates the fact data to the required grain (monthly totals per account), significantly reducing the number of rows in the fact table and thus model size.

In the Transactions query, adding a column that contains the MonthStartDate value for TransactionDate would add a redundant column if a proper Date dimension is used and related, and it does not reduce model size; it potentially increases it.

Creating many-to-many relationships violates the star schema modeling approach, which primarily relies on one-to-many relationships between dimension and fact tables for optimal performance and simplicity.