You have a database named DB1 that contains millions of rows.You plan to perform a weekly audit of the changes to the rows.You need to ensure that you can view which rows were modified and the hour that the modification occurred.What should you do?
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 the series.Start of repeated scenario -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. TheDimension.SalesTerritory and Dimension.Customer tables are frequently updated. The Fact.Order table is optimized for weekly reporting, but the company wants to change it to 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 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.The following requirements must be met:✑ Implement table partitioning to improve the manageability of the data warehouse and to avoid the need to repopulate all transactional data each night. Use a partitioning strategy that is as granular as possible.✑ Partition the Fact.Order table and retain a total of seven years of data.✑ Partition the Fact.Ticket table and retain seven years of data. At the end of each month, the partition structure must apply a sliding window strategy to ensure that a new partition is available for the upcoming month, and that the oldest month of data is archived and removed.✑ Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.✑ Incrementally load all tables in the database and ensure that all incremental changes are processed.✑ Maximize the performance during the data loading process for the Fact.Order partition.✑ Ensure that historical data remains online and available for querying.✑ Reduce ongoing storage costs while maintaining query performance for current data.You are not permitted to make changes to the client applications.End of repeated scenario -You need to implement the data partitioning strategy.How should you partition the Fact.Order table?
DRAG DROP -Table1 -Table1 -You are designing an indexing strategy for a data warehouse. The data warehouse contains a table named. Data is bulk inserted into.You plan to create the indexes configured as shown in the following table.Which type of index should you use to minimize the query times of each index? To answer, drag the appropriate index types to the correct indexes. Each index type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.Select and Place:
HOTSPOT -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 the series.Start of repeated scenario -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. TheDimension.SalesTerritory and Dimension.Customer tables are frequently updated. The Fact.Order table is optimized for weekly reporting, but the company wants to change it to 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.The following requirements must be met:✑ Implement table partitioning to improve the manageability of the data warehouse and to avoid the need to repopulate all transactional data each night. Use a partitioning strategy that is as granular as possible.✑ Partition the Fact.Order table and retain a total of seven years of data.✑ Partition the Fact.Ticket table and retain seven years of data. At the end of each month, the partition structure must apply a sliding window strategy to ensure that a new partition is available for the upcoming month, and that the oldest month of data is archived and removed.✑ Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.✑ Incrementally load all tables in the database and ensure that all incremental changes are processed.✑ Maximize the performance during the data loading process for the Fact.Order partition.✑ Ensure that historical data remains online and available for querying.✑ Reduce ongoing storage costs while maintaining query performance for current data.You are not permitted to make changes to the client applications.End of repeated scenario -You need to optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date tables.Which technology should you use for each table?To answer, select the appropriate technologies in the answer area.Hot Area:
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.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.Each night you receive a comma separated values (CSV) file that contains different types of rows. Each row type has a different structure. Each row in the CSV file is unique. The first column in every row is named Type. This column identifies the data type.For each data type, you need to load data from the CSV file to a target table. A separate table must contain the number of rows loaded for each data type.Solution: You create a SQL Server Integration Services (SSIS) package as shown in the exhibit. (Click the Exhibit tab.)Does the solution meet the goal?
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.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.Each night you receive a comma separated values (CSV) file that contains different types of rows. Each row type has a different structure. Each row in the CSV file is unique. The first column in every row is named Type. This column identifies the data type.For each data type, you need to load data from the CSV file to a target table. A separate table must contain the number of rows loaded for each data type.Solution: You create a SQL Server Integration Services (SSIS) package as shown in the exhibit. (Click the Exhibit tab.)Does the solution meet the goal?
