What change should you make?

Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.

You have a Microsoft SQL Server data warehouse instance that supports several client applications.

The data warehouse includes the following tables: Dimension.SalesTerritory, Dimension.Customer, Dimension.Date, Fact.Ticket, and Fact.Order. The Dimension.SalesTerritory and Dimension.Customer tables are frequently updated. The Fact.Order table is optimized for weekly reporting, but the company wants to change it daily. The Fact.Order table is loaded by using an ETL process. Indexes have been added to the table over time, but the presence of these indexes slows data loading.

All data in the data warehouse is stored on a shared SAN. All tables are in a database named DB1. You have a second database named DB2 that contains copies of production data for a development environment. The data warehouse has grown and the cost of storage has increased. Data older than one year is accessed infrequently and is considered historical.

You have the following requirements:

You are not permitted to make changes to the client applications.

You need to optimize the storage for the data warehouse.

What change should you make?
A . Partition the Fact.Order table, and move historical data to new filegroups on lower-cost storage.
B . Create new tables on lower-cost storage, move the historical data to the new tables, and then shrink the database.
C . Remove the historical data from the database to leave available space for new data.
D . Move historical data to new tables on lower-cost storage.

View Answer

Answer: A

Explanation:

Create the load staging table in the same filegroup as the partition you are loading.

Create the unload staging table in the same filegroup as the partition you are deleteing.

From scenario: Data older than one year is accessed infrequently and is considered historical.

References: https://blogs.msdn.microsoft.com/sqlcat/2013/09/16/top-10-best-practices-for-building-a-large-scale-relational-data-warehouse/