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Question 1:

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 color.

You need to create a dimension that contains all of the colors for products sold by the company.

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

Correct Answer: 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. References: https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional- models-olap-logical-cube-objects/dimension-relationships


Question 2:

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:

You need to create a new dimension that allows users to list shipments by the country where the product is shipped. Which relationship type should you use between the Shipment table and the new dimension?

A. no relationship

B. regular

C. fact

D. referenced

E. many-to-many

F. data mining

Correct Answer: 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


Question 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. 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: Change the storage mode for the data model to DirectQuery.

Does the solution meet the goal?

A. Yes

B. No

Correct Answer: A

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:

Data is up-to-date, and there is no extra management overhead of having to maintain a separate copy of the data (in the in-memory cache). Changes to the underlying source data can be immediately reflected in queries against the data

model. Datasets can be larger than the memory capacity of an Analysis Services server.

Etc.

References:https://docs.microsoft.com/en-us/sql/analysis-services/tabular- models/directquery-mode-ssas-tabular


Question 4:

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

Correct Answer: B

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


Question 5:

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 DirectQuery with InMemory.

Does the solution meet the goal?

A. Yes

B. No

Correct Answer: A

With DirectQuerywithInMemory mode the queries use the relational data source 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


Latest 70-768 Dumps70-768 VCE Dumps70-768 Braindumps

Question 6:

You are troubleshooting query performance for a SQL Server Analysis Services (SSAS) cube.

A user reports that a Multidimensional Expressions (MDX) query is very slow.

You need to identify the MDX query statement in a trace by using SQL Server Profiler.

Which event class should you use?

A. Get Data From Aggregation

B. Query Subcube

C. Query Begin

D. Progress Report Begin

E. Calculate Non Empty Begin

F. Execute MDX Script Begin

Correct Answer: C


Question 7:

You need to configure the server to optimize the afternoon report generation based on the OrderAnalysis cube. Which property should you configure?

A. LowMemoryLimit

B. VertiPaqPagingPolicy

C. TotalMemoryLimit

D. VirtualMemoryLimit

Correct Answer: A

LowMemoryLimit: For multidimensional instances, a lower threshold at which the server first begins releasing memory allocated to infrequently used objects.

From scenario: 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.


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