Microsoft Developing SQL Data Models (070-768日本語版) - 070-768日本語 Exam Questions

Explanation:
Step 1: Open the cube editor, and open the Dimension Usage tab.
Step 2: Configure a relationship between the Customer dimension and the Sales measure group.
Use Day as the granularity.
From scenario: The SalesAnalysis cube contains a fact table named CoffeeSale loaded from a table named FactSale in the data warehouse. The time granularity within the cube is 15 minutes.
The cube is processed every night at 23:00. You determine that the fact table cannot be fully processed in the expected time. Users have reported slow query response times.
Step 3: Reprocess the cube.
Step 4: Deploy the project changes.
Topic 1, Background
Wide World Importers has multidimensional cubes named SalesAnalysis and ProductSales. The SalesAnalysis cube is refreshed from a relational data warehouse. You have a Microsoft SQL Server Analysis Services instance that is configured to use tabular mode. You have a tabular data model named CustomerAnalysis.
Sales Analysis
The SalesAnalysis cube contains a fact table named CoffeeSale loaded from a table named FactSale in the data warehouse. The time granularity within the cube is 15 minutes. The cube is processed every night at 23:00. You determine that the fact table cannot be fully processed in the expected time. Users have reported slow query response times.
The SalesAnalysis model contains tables from a SQL Server database named SalesDB. You set the DirectQueryMode option to DirectQuery. Data analyst access data from a cache that is up to
24 hours old. Data analyst report performance issues when they access the SalesAnalysis model.
When analyzing sales by customer, the total of all sales is shown for every customer, instead of the customer's sales value. When analyzing sales by product, the correct totals for each product are shown.
Customer Analysis
You are redesigning the CustomerAnalysis tabular data model that will be used to analyze customer sales. You plan to add a table named CustomerPermission to the model. This table maps the Active Directory login of an employee with the CustomerId keys for all customers that the employee manages.
The CustomerAnalysis data model will contain a large amount of data and needs to be shared with other developers even if a deployment fails. Each time you deploy a change during development, processing takes a long time.
Data analysts must be able to analyze sales for financial years, financial quarters, months, and days. Many reports are based on analyzing sales by month.
Product Sales
The ProductSales cube allows data analysts to view sales information by product, city, and time.
Data analysts must be able to view ProductSales data by Year to Date (YTD) as a measure. The measure must be formatted as currency, associated with the Sales measure group, and contained in a folder named Calculations.
Requirements
You identify the following requirements:
- Data available during normal business hours must always be up-to-
date.
- Processing overhead must be minimized.
- Query response times must improve.
- All queries that access the SalesAnalysis model must use cached data
by default.
- Data analysts must be able to access data in near real time.

Explanation:
Box 1:EVALUATE
Box 2:SUMMERIZE
Box 3:ROLLUP
Box 4:'Product Subcategory' ['Product Subcategory Name]
Note: The behavior of SUMMARIZE is similar to the GROUP BY syntax of a SELECT statement in SQL. For example, consider the following query.
EVALUATE
SUMMARIZE(
'Internet Sales',
'Internet Sales'[Order Date],
"Sales Amount", SUM( 'Internet Sales'[Sales Amount] ) )
This query calculates the total of Sales Amount for each date in which there is at least one order, producing this result.

Explanation: Only visible for Pass4Test members. You can sign-up / login (it's free).