DRAG DROP

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 tochange 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:
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.
You need to implement partitioning for the Fact.Ticket table.
Which three actions should you perform in sequence? To answer, drag the appropriate actions to the correct locations. Each action 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.
NOTE: More than one combination of answer choices is correct. You will receive credit for any of the correct combinations you select.
Select and Place:

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. table using the ALTER TABLE (Transact-SQL) statement with the SWITCH PARTITION argument with SPLIT RANGE.

DRAG DROP

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:
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.
You need to configure the Fact.Order table.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Select and Place:

performance during the data loading process for the Fact.Order partition.

DRAG DROP

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:
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.
You need to optimize data loading for the Dimension.Customer table.
Which three Transact-SQL segments should you use to develop the solution? To answer, move the appropriate
Transact-SQL segments from the list of Transact-SQL segments to the answer area and arrange them in the correct order.
NOTE: You will not need all of the Transact-SQL segments.
Select and Place:

contains copies of production data for a development environment. database, use the sys.sp_cdc_enable_db stored procedure. sys.sp_cdc_enable_db has no parameters. sys.sp_cdc_enable_table https://docs.microsoft.com/en-us/sql/relational-databases/system-stored-procedures/sys-sp-cdc-enable-dbtransact-sql

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:
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.
You need to implement the data partitioning strategy.
How should you partition the Fact.Order table?

You are developing a Microsoft SQL Server Integration Services (SSIS) projects. The project consists of several packages that load data warehouse tables.
You need to extend the control flow design for each package to use the following control flow while minimizing development efforts and maintenance:

Solution: You add the control flow to a control flow package part. You add an instance of the control flow package part to each data warehouse load package.
Does the solution meet the goal? package by using the Control Flow tab in SSIS Designer.

You are developing a Microsoft SQL Server Integration Services (SSIS) projects. The project consists of several packages that load data warehouse tables.
You need to extend the control flow design for each package to use the following control flow while minimizing development efforts and maintenance:

Solution: You add the control flow to an ASP.NET assembly. You add a script task that references this assembly to each data warehouse load package.
Does the solution meet the goal? package by using the Control Flow tab in SSIS Designer.

You are developing a Microsoft SQL Server Integration Services (SSIS) projects. The project consists of several packages that load data warehouse tables.
You need to extend the control flow design for each package to use the following control flow while minimizing development efforts and maintenance:

Solution: You add the control flow to a script task. You add an instance of the script task to the storage account in Microsoft Azure.
Does the solution meet the goal? package by using the Control Flow tab in SSIS Designer.

You have the following line-of-business solutions:
ERP system
Online WebStore
Partner extranet
One or more Microsoft SQL Server instances support each solution. Each solution has its own product catalog.
You have an additional server that hosts SQL Server Integration Services (SSIS) and a data warehouse. You populate the data warehouse with data from each of the line-of-business solutions. The data warehouse does not store primary key values from the individual source tables.
The database for each solution has a table named Products that stored product information. The Products table in each database uses a separate and unique key for product records. Each table shares a column named
ReferenceNr between the databases. This column is used to create queries that involve more than once solution.You need to load data from the individual solutions into the data warehouse nightly. The following requirements must be met:
If a change is made to the ReferenceNr column in any of the sources, set the value of IsDisabled to True and create a new row in the Products table.
If a row is deleted in any of the sources, set the value of IsDisabled to True in the data warehouse.
Solution: Perform the following actions:
Enable the Change Tracking for the Product table in the source databases.
Query the cdc.fn_cdc_get_all_changes_capture_dbo_products function from the sources for updated rows.
Set the IsDisabled column to True for rows with the old ReferenceNr value.
Create a new row in the data warehouse Products table with the new ReferenceNr value.
Does the solution meet the goal?

You have the following line-of-business solutions:
ERP system
Online WebStore
Partner extranetOne or more Microsoft SQL Server instances support each solution. Each solution has its own product catalog.
You have an additional server that hosts SQL Server Integration Services (SSIS) and a data warehouse. You populate the data warehouse with data from each of the line-of-business solutions. The data warehouse does not store primary key values from the individual source tables.
The database for each solution has a table named Products that stored product information. The Products table in each database uses a separate and unique key for product records. Each table shares a column named
ReferenceNr between the databases. This column is used to create queries that involve more than once solution.
You need to load data from the individual solutions into the data warehouse nightly. The following requirements must be met:
If a change is made to the ReferenceNr column in any of the sources, set the value of IsDisabled to True and create a new row in the Products table.
If a row is deleted in any of the sources, set the value of IsDisabled to True in the data warehouse.
Solution: Perform the following actions:
Enable the Change Tracking feature for the Products table in the three source databases.
Query the CHANGETABLE function from the sources for the deleted rows.
Set the IsDIsabled column to True on the data warehouse Products table for the listed rows.
Does the solution meet the goal?

You have the following line-of-business solutions:
ERP system
Online WebStore
Partner extranet
One or more Microsoft SQL Server instances support each solution. Each solution has its own product catalog.
You have an additional server that hosts SQL Server Integration Services (SSIS) and a data warehouse. You populate the data warehouse with data from each of the line-of-business solutions. The data warehouse does not store primary key values from the individual source tables.
The database for each solution has a table named Products that stored product information. The Products table in each database uses a separate and unique key for product records. Each table shares a column named
ReferenceNr between the databases. This column is used to create queries that involve more than once solution.
You need to load data from the individual solutions into the data warehouse nightly. The following requirements must be met:
If a change is made to the ReferenceNr column in any of the sources, set the value of IsDisabled to True and create a new row in the Products table.
If a row is deleted in any of the sources, set the value of IsDisabled to True in the data warehouse.
Solution: Perform the following actions:
Enable the Change Tracking for the Product table in the source databases.
Query the CHANGETABLE function from the sources for the updated rows.
Set the IsDisabled column to True for the listed rows that have the old ReferenceNr value.
Create a new row in the data warehouse Products table with the new ReferenceNr value.
Does the solution meet the goal?