Microsoft 70-767 - Implementing a SQL Data Warehouse Exam
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Total 147 questions
Question #11 (Topic: Topic 1)
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.
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:
[Microsoft-70-767-1.0/xmlfile-14_1.jpg]
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?
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.
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:
[Microsoft-70-767-1.0/xmlfile-14_1.jpg]
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?
A. Yes
B. No
Answer: B
Question #12 (Topic: Topic 1)
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.
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:
[Microsoft-70-767-1.0/xmlfile-16_1.jpg]
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?
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.
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:
[Microsoft-70-767-1.0/xmlfile-16_1.jpg]
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?
A. Yes
B. No
Answer: A
Question #13 (Topic: Topic 1)
DRAG DROP
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 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.
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:
[Microsoft-70-767-1.0/xmlfile-18_1.png]
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 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.
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:
[Microsoft-70-767-1.0/xmlfile-18_1.png]
Answer: [Microsoft-70-767-1.0/xmlfile-18_2.png]
Question #14 (Topic: Topic 1)
DRAG DROP
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 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.
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:
[Microsoft-70-767-1.0/xmlfile-20_1.png]
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 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.
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:
[Microsoft-70-767-1.0/xmlfile-20_1.png]
Answer: [Microsoft-70-767-1.0/xmlfile-21_1.png]
Question #15 (Topic: Topic 1)
DRAG DROP
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 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.
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:
[Microsoft-70-767-1.0/xmlfile-23_1.png]
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 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.
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:
[Microsoft-70-767-1.0/xmlfile-23_1.png]
Answer: [Microsoft-70-767-1.0/xmlfile-24_1.png]
