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070-767 PDF DEMO:

QUESTION NO: 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 have a Microsoft SQL server that has Data Quality Services (DQS) installed.
You need to review the completeness and the uniqueness of the data stored in the matching policy.
Solution: You profile the data.
Does this meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Use a matching rule.
References: https://docs.microsoft.com/en-us/sql/data-quality-services/create-a-matching- policy?view=sql-server-2017

QUESTION NO: 2
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.
Your company uses Microsoft SQL Server to deploy a data warehouse to an environment that has a
SQL Server Analysis Services (SSAS) instance. The data warehouse includes the Fact.Order table as shown in the following table definition. The table has no indexes.
You need to ensure that the queries complete as quickly as possible.
Solution: You create measure for the Fact.Order table. Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
You should use a columnstore index.
Columnstore indexes are the standard for storing and querying large data warehousing fact tables.
This index uses column-based data storage and query processing to achieve gains up to 10 times the query performance in your data warehouse over traditional row-oriented storage.
References: https://docs.microsoft.com/en-us/sql/relational-databases/indexes/columnstore- indexes-overview?view=sql-server-2017

QUESTION NO: 3
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 sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a data warehouse that stores information about products, sales, and orders for a manufacturing company. The instance contains a database that has two tables named
SalesOrderHeader and SalesOrderDetail. SalesOrderHeader has 500,000 rows and SalesOrderDetail has 3,000,000 rows.
Users report performance degradation when they run the following stored procedure:
You need to optimize performance.
Solution: You run the following Transact-SQL statement:
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
100 out of 500,000 rows is a too small sample size.
References: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse- tables-statistics

QUESTION NO: 4
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. Move historical data to new tables 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. Partition the Fact.Order table, and move historical data to new filegroups on lower-cost storage.
Answer: D
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/

QUESTION NO: 5
HOTSPOT
You have a Microsoft SQL Server Data Warehouse instance that uses SQL Server Analysis Services
(SSAS). The instance has a cube containing data from an on-premises SQL Server instance. A measure named Measure1 is configured to calculate the average of a column.
You plan to change Measure1 to a full additive measure and create a new measure named Measure2 that evaluates data based on the first populated row.
You need to configure the measures.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Box 1:
The default setting is SUM (fully additive).
Box 2:
FirstNonEmpty: The member value is evaluated as the value of its first child along the time dimension that contains data.
References: https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional- models/define-semiadditive-behavior

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