A.   Oracle Essbase

B.   IBM TM1

C.   Microsoft SSAS

A.   It is same as data validation

B.   Harmonization of data cannot be considered as Data Scrubbing

D.   Standardization of data cannot be considered as Data Scrubbing

A.   Snowflake Schema

C.   Star Schema

D.   Fact Constellation Schema

B.   Merging the cells of all but one dimension

C.   Merging the cells along one dimension

D.   Selecting all but one dimension of the data cube.

A.   To maintain data history

B.   To enhance data quality

D.   To integrate data from multiple source systems

A.   ODS

B.   Cleanroom Table

D.   No distinct cleansing phase, data cleansed during MDX queries

A.   It uses the two level of data storage representation to handle dense and sparse data sets

C.   It facilitates OLTP operations in SQL

D.   It uses array-based multidimensional storage engines

A.   It is necessary to keep the operational data free of any warehouse operations

B.   It contains data derived from multiple sources

C.   A data warehouse cannot afford to allow corrupted data within it

B.   randomizing the year and region

C.   specifying a particular year

D.   randomizing the year

B.   core table

C.   metadata table

D.   dimension table

A.   Can handle large amounts of data

B.   Performance can be slow

D.   Can leverage functionalities inherent in the relational database

B.   Data warehouses contain data that is generally loaded from the operational databases on a regular interval

C.   Time horizon of a data warehouse is significanlty longer than that of operational systems

D.   Data Warehouse maintains both historical and (nearly) current data

A.   MOLAP cubes are built for fast data retrieval

B.   MOLAP cubes are optimal for slicing and dicing operations

C.   Data is stored in a multidimensional cube.

A.   It allows users to analyze data from many different dimensions or angles

C.   It is the process of analyzing data from different perspectives and summarizing it into useful information

D.   It is the process of finding correlations or patterns among various fields

A.   SQL

B.   ETL

D.   OLTP

A.   Incremental Extraction

C.   Full Extraction

D.   Online extraction

B.   It is copy of transaction data specifically structured for query and analysis

C.   It is designed to facilitate reporting and analysis

D.   It is a non-volatile time-variant repository

A.   It is represented by centralized fact tables

B.   Its a logical arrangement of tables in a multidimensional database

D.   It is a variation of the star schema

B.   Extract Test Language

C.   Export, Transmit, Load

D.   Export, Translate, Load

A.   Star Schema

B.   Snowflake Schema

C.   Fact Constellation Schema

A.   OLTP

B.   Relational Database

C.   ODS

A.   Star Schema

C.   OLAP

D.   Snowflake Schema

A.   When the two sources have multiple matching columns

B.   When the two sources have a primary-key to foreign-key relationship

D.   When the two sources are heterogeneous

B.   the raw data from which the facts are derived

C.   the direction along which additive measures can be combined

D.   the ratio of facts to dimensions

A.   Drill-up

B.   Roll-down

C.   Roll-up

B.   AllMembers

C.   Leaves

D.   Distinct

A.   Fortran

C.   SQL

D.   SPSS

A.   dice

B.   join

C.   pivot

A.   transaction volume

B.   gross profits

C.   costs

A.   It rotates the data axes in view in order to provide an alternative presentation of data

B.   Two consecutive slice operations in two different dimensions

C.   It is also known as rotation

A.   CRC

C.   (none of these choices)

D.   audit columns

A.   /* this line */

B.   // this line

D.   -- this line

B.   an online, open exchange in which organizations can trade business information

C.   a schema that organizes data into facts and dimensions

D.   a central repository where separate organizations can securely backup data

A.   Star and snowflake schema contains two Fact tables

B.   Snowflake schema contains two Fact tables

D.   Star schema contains two Fact tables

B.   Store UID when a Store dimension table exists

C.   Units sold

D.   None of the Above

A.   Subject Oriented

C.   Nonvolatile

D.   Integrated

A.   Database

B.   OLAP

D.   Data Warehousing env

A.   Roll-up

B.   Roll-down

D.   Drill-up

B.   OLTP

C.   Both OLAP and OLTP

D.   Neither OLAP nor OLTP

B.   None of the Above

C.   a three-dimensional array for Online Analytical Processing

D.   a dimensional-reduction operation that summarizes data

A.   normalization

B.   All of the Above

D.   primary keys

A.   All of the options are correct

B.   OLAP

C.   Data Warehousing env

A.   It uses just one level of data storage representation to handle sparse data sets

B.   It uses just one level of data storage representation to handle dense data sets

C.   It uses one level of data storage representation to handle both dense and sparse data sets

B.   It forms a new sub-cube by selecting one or more dimensions

C.   The Dice operation performs selection of one dimension on a given cube

D.   It navigates the data from less detailed data to highly detailed data

A.   dice

B.   slice

C.   rotating

A.   normalized approach

B.   Neither dimensional nor normalized approach

C.   Both dimensional and normalized approach

B.   MOSTLY8, Runlength, Raw

C.   Byte-dictionary, LZO, Delta

D.   LZO, Delta, Raw

A.   Read and Write

C.   Write Only

D.   Write Deconditional

A.   Integrated data warehouse

C.   Offline operational data warehouse

D.   Offline data warehouse

A.   Relational

B.   Multidimensional

C.   Hybrid