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.

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You have a data warehouse that stores sales data. One fact table has 100 million rows.

You must reduce storage needs for the data warehouse.

You need to implement a solution that uses column-based storage and

provides real-time analytics for the operational workload.

Solution: You remove any clustered indexes and load the table for processing.

Does the solution meet the goal?

A. Yes

B. No

Explanation:

Clustered columnstore tables offer both the h

ighest level of data compression as well as the best overall query performance. Clustered columnstore tables will generally outperform clustered index or heap tables and are usually the best choice for large tables. For these reasons, clustered columnstore

is the best place to start when you are unsure of how to index your table.

Note: Dimensional tables can be used to reduce the size of fact tables.

Dimension tables contain attribute data that might change but usually changes infrequently. For example, a

customers name and address are stored in a dimension table and updated only when the customers profile changes. To minimize the size of a large fact table, the customers name and address do not need to be in every row of a fact table. Instead, the fact

table and the dimension table can share a customer ID. A query can join the two tables to associate a customers profile and transactions.

References: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-overview

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