http://https://www.lead1pass.com/Microsoft/70-768-practice-exam-dumps.html (50 Q&As Dumps, 30%OFF Special Discount: 30free )
NEW QUESTION NO: 7
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You have a Microsoft SQL Server Analysis Services (SSAS) instance that is configured to use multidimensional mode. You create the following cube:
Users need to be able to analyze sales by product and color.
You need to create the dimension.
Which relationship type should you use between the InternetSales table and the new dimension?
A. no relationship
B. regular
C. fact
D. referenced
E. many-to-many
F. data mining
Answer: D
Explanation/Reference:
Explanation:
A reference dimension relationship between a cube dimension and a measure group exists when the key column for the dimension is joined indirectly to the fact table through a key in another dimension table, as shown in the following illustration.
A reference dimension relationship represents the relationship between dimension tables and a fact table in a snowflake schema design. When dimension tables are connected in a snowflake schema, you can define a single dimension using columns from multiple tables, or you can define separate dimensions based on the separate dimension tables and then define a link between them using the reference dimension relationship setting. The following figure shows one fact table named InternetSales, and two dimension tables called Customer and Geography, in a snowflake schema.
You can create two dimensions related to the InternetSales measure group: a dimension based on the Customer table, and a dimension based on the Geography table. You can then relate the Geography dimension to the InternetSales measure group using a reference dimension relationship using the Customer dimension.
Incorrect Answers:
B: A regular dimension relationship between a cube dimension and a measure group exists when the key column for the dimension is joined directly to the fact table.
C: Fact dimensions, frequently referred to as degenerate dimensions, are standard dimensions that are constructed from attribute columns in fact tables instead of from attribute columns in dimension tables.
E: Many to Many Dimension Relationships.
In most dimensions, each fact joins to one and only one dimension member, and a single dimension member can be associated with multiple facts. In relational database terminology, this is referred to as a one-to-many relationship. However, it is frequently useful to join a single fact to multiple dimension members. For example, a bank customer might have multiple accounts (checking, saving, credit card, and investment accounts), and an account can also have joint or multiple owners. The Customer dimension constructed from such relationships would then have multiple members that relate to a single account transaction.
References: https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional-models-olap-logical- cube-objects/dimension-relationships
NEW QUESTION NO: 8
You are building a Microsoft SQL Server Analysis Services multidimensional model over a SQL Server database. In a cube named OrderAnalysis, there is a standard cube dimension named Stock Item.
This dimension has the following attributes:
Stock Item Key
WWI Stock Item ID
Stock Item
Color
Selling Package
Buying Package
Brand
Size
Lead Time Days
Quantity Per Outer
Is Chiller Stock
Barcode
Tax Rate
Unit Price
Recommended Retail Price
Typical Weight Per Unit
Photo
Valid From
Valid To
Lineages Key
Users report that the attributes Stock Item Key and Photo are distracting and are not providing any value.
They have asked for the attributes to be removed. However, these attributes are needed by other cubes.
You need to hide the specified attributes from the end users of the OrderAnalysis cube. You do not want to change the structure of the dimension.
Which change should you make to the properties for the Stock Item Key and Photo attributes?
A. Set the AttributeHierarchyVisible property to False.
B. Set the AttributeHierarchyEnabledproperty to False.
C. Set the AttributeVisibility property to Hidden.
D. Set the Usage property to Regular.
E. Set the AttributeHierarchyDisplayFolder property to Hidden.
Answer: A
Explanation/Reference:
Explanation:
The value of the AttributeHierarchyEnabled property determines whether an attribute hierarchy is created.
If this property is set to False, the attribute hierarchy is not created and the attribute cannot be used as a level in a user hierarchy; the attribute hierarchy exists as a member property only. However, a disabled attribute hierarchy can still be used to order the members of another attribute. If the value of the AttributeHierarchyEnabled property is set to True, the value of the AttributeHierarchyVisible property determines whether the attribute hierarchy is visible independent of its use in a user-defined hierarchy.
References:https://technet.microsoft.com/en-us/library/ms166717(v=sql.110).aspx
NEW QUESTION NO: 9
You are optimizing a Microsoft SQL Server Analysis Services (SSAS) multidimensional model over a SQL Server database. You have a table named City which has several dimensions that do not contain a space in their names. One dimension is named SalesTerritory rather than Sales Territory.
You need to ensure that Report developers can drag the attribute name to the report rather than having to re-label the attributes by implementing spaces. You must minimize administrative effort and not break any upstream processes.
What should you do?
A. In the SQL Server database, run the system procedure sp_rename to rename the columns in the base tables with the target name.
B. In SQL Server Management Studio, navigate to the City table, expand the columns, press F2, and rename the columns in the base tables.
C. In the SQL Server database, implement a SYNONYM.
D. In the SQL Server database, implement a view over the City table that aliases the columns in the tables.
Answer: D
Explanation/Reference:
Explanation:
NEW QUESTION NO: 10
DRAG DROP
You are a business analyst for a company that uses a Microsoft SQL Server Analysis Services (SSAS) tabular database for reporting. The database model contains the following tables:
You have been asked to write a query for a report that returns the total sales for each product subcategory, as well as for each product category.
You need to write the query to return the data for the report.
How should you complete the DAX statement? To answer, drag the appropriate DAX segment to the correct locations. Each DAX segment 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:
Answer:
Explanation/Reference:
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.
Reference: https://msdn.microsoft.com/en-us/library/gg492171.aspx
NEW QUESTION NO: 11
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You deploy a tabular data model to an instance of Microsoft SQL Server Analysis Services (SSAS). The model uses an in-memory cache to store and query data. The data set is already the same size as the available RAM on the server. Data volumes are likely to continue to increase rapidly.
Your data model contains multiple calculated tables.
The data model must begin processing each day at 2:00 and processing should be complete by 4:00 the same day. You observe that the data processing operation often does not complete before 7:00. This is adversely affecting team members.
You need to improve the performance.
Solution: Install solid-state disk drives to store the tabular data model.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation/Reference:
Explanation:
By default, tabular models use an in-memory cache to store and query data. When tabular models query data residing in-memory, even complex queries can be incredibly fast. However, there are some limitations to using cached data. Namely, large data sets can exceed available memory, and data freshness requirements can be difficult if not impossible to achieve on a regular processing schedule.
DirectQuery overcomes these limitations while also leveraging RDBMS features making query execution more efficient.
With DirectQuery: +
References:https://docs.microsoft.com/en-us/sql/analysis-services/tabular-models/directquery-mode-ssas- tabular
NEW QUESTION NO: 12
HOTSPOT
You are deploying a multidimensional Microsoft SQL Server Analysis Services (SSAS) project. You add two new role-playing dimensions named Picker and Salesperson to the cube. Both of the cube dimensions are based upon the underlying dimension named Employee in the data source view.
Users report that they are unable to differentiate the Salesperson attributes from the Picker attributes.
You need to ensure that the Salesperson and Picker attributes in each dimension use unique names.
In the table below, identify an option that you would use as part of the process to alter the names of the attributes for each of the dimensions.
NOTE: Make only one selection in each column.
Hot Area:
Answer:
Explanation/Reference:
A named query is a SQL expression represented as a table. In a named query, you can specify an SQL expression to select rows and columns returned from one or more tables in one or more data sources. A named query is like any other table in a data source view (DSV) with rows and relationships, except that the named query is based on an expression.
A named query lets you extend the relational schema of existing tables in DSV without modifying the underlying data source.
References: https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional-models/define- named-queries-in-a-data-source-view-analysis-services
NEW QUESTION NO: 13
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
A company has an e-commerce website. When a customer places an order, information about the transaction is inserted into tables in a Microsoft SQL Server relational database named OLTP1. The company has a SQL Server Analysis Services (SSAS) instance that is configured to use Tabular mode.
SSAS uses data from OLTP1 to populate a data model.
Sales analysts build reports based on the SSAS model. Reports must be able to access data as soon as it is available in the relational database.
You need to configure and deploy an Analysis Services project to the Analysis Services instance that allows near real-time data source access.
Solution: In the Deployment Option property for the report, you set the Query Mode to InMemory with DirectQuery.
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation/Reference:
Explanation:
With InMemory with DirectQuery: Queries use the cache by default, unless otherwise specified in the connection string from the client.
References: https://msdn.microsoft.com/en-us/library/hh230898(v=sql.120).aspx
NEW QUESTION NO: 14
You are a business analyst for a retail company that uses a Microsoft SQL Server Analysis Services (SSAS) multidimensional database for reporting. The database contains the following objects:
You must create a report that shows, for each month, the Internet sales for that month and the total Internet sales for the calendar year up to and including the current month.
You create the following MDX statement (Line numbers are included for reference only.):
You need to complete the MDX statement to return data for the report.
Which MDX segment should you use in line 01?
A:
B:
C:
D:
A. Option A
B. Option B
C. Option C
D. Option D
Answer: B
Explanation/Reference:
Explanation:
The following example returns the sum of the Measures. [Order Quantity] member, aggregated over the first eight months of calendar year 2003 that are contained in the Date dimension, from the Adventure Works cube.
Copy
WITH MEMBER [Date].[Calendar].[First8Months2003] AS
Aggregate(
PeriodsToDate(
[Date].[Calendar].[Calendar Year],
[Date].[Calendar].[Month].[August 2003]
)
)
SELECT
[Date].[Calendar].[First8Months2003] ON COLUMNS,
[Product].[Category].Children ON ROWS
FROM
[Adventure Works]
WHERE
[Measures].[Order Quantity]
References:https://docs.microsoft.com/en-us/sql/mdx/aggregate-mdx
NEW QUESTION NO: 15
DRAG DROP
Case Study #2
This is a case study. Case studies are not limited separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other question on this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next sections of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question on this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
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.
You need to configure the CoffeeSale fact table environment.
Which four 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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Select and Place:
Answer:
Explanation/Reference:
Step 1: Partition the CoffeSale facto table.
Step 2: Set the storage mode for all partitions to HOLAP.
Partitions stored as HOLAP are smaller than the equivalent MOLAP partitions because they do not contain source data and respond faster than ROLAP partitions for queries involving summary data.
Step 3: Alter the processing job to ensure that it rearranges the partition structure each evening.
Step 4: Test that the cube meets the functional requirement for data currency and query performance.
From scenario:
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.
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.
References:https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional-models-olap-logical- cube-objects/partitions-partition-storage-modes-and-processing
NEW QUESTION NO: 16
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution. Determine whether the solution meets the stated goals.
You have an existing multidimensional cube that provides sales analysis. The users can slice by date, product, location, customer, and employee.
The management team plans to evaluate sales employee performance relative to sales targets. You identify the following metrics for employees:
Ninety percent or greater relative to sales target values is considered on target.
Between 75 percent and 90 percent is considered slightly off target.
Below 75 percent is considered off target.
You need to implement the KPI based on the Status expression.
Solution: You design the following solution:
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation/Reference:
Explanation:
NEW QUESTION NO: 17
DRAG DROP
Case Study #1
This is a case study. Case studies are not limited separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other question on this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next sections of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question on this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Background
Wide World Importers imports and sells clothing. The company has a multidimensional Microsoft SQL Server Analysis Services instance. The server has 80 gigabytes (GB) of available physical memory. The following installed services are running on the server:
SQL Server Database Engine
SQL Server Analysis Services (multidimensional)
The database engine instance has been configured for a hard cap of 50 GB, and it cannot be lowered. The instance contains the following cubes: SalesAnalysis, OrderAnalysis.
Reports that are generated based on data from the OrderAnalysis cube take more time to complete when they are generated in the afternoon each day. You examine the server and observe that it is under significant memory pressure.
Processing for all cubes must occur automatically in increments. You create one job to process the cubes and another job to process the dimensions. You must configure a processing task for each job that optimizes performance. As the cubes grown in size, the overnight processing of the cubes often do not complete during the allowed maintenance time window.
SalesAnalysis
The SalesAnalysis cube is currently being tested before being used in production. Users report that day name attribute values are sorted alphabetically. Day name attribute values must be sorted chronologically.
Users report that they are unable to query the cube while any cube processing operations are in progress.
You need to maximize data availability during cube processing and ensure that you process both dimensions and measures.
OrderAnalysis
The OrderAnalysis cube is used for reporting and ad-hoc queries from Microsoft Excel. The data warehouse team adds a new table named Fact.Transaction to the cube. The Fact.Transaction table includes a column named Total Including Tax. You must add a new measure named Transactions - Total Including Tax to the cube. The measure must be calculated as the sum of the Total Including Tax column across any selected relevant dimensions.
Finance
The Finance cube is used to analyze General Ledger entries for the company.
Requirements
You must minimize the time that it takes to process cubes while meeting the following requirements:
The Sales cube requires overnight processing of dimensions, cubes, measure groups, and partitions.
The OrderAnalysis cube requires overnight processing of dimensions only.
The Finance cube requires overnight processing of dimensions only.
You need to create the cube processing job and the dimension processing job.
Which processing task should you use for each job? To answer, drag the appropriate processing tasks to the correct locations. Each processing task 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:
Answer:
Explanation/Reference:
Box 1: ProcessData
Processes data only without building aggregations or indexes. If there is data is in the partitions, it will be dropped before re-populating the partition with source data.
Box 2: Process Update
Forces a re-read of data and an update of dimension attributes. Flexible aggregations and indexes on related partitions will be dropped.
References:https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional-models/processing- options-and-settings-analysis-services