OLAP (Online Analytical Processing) MCQs

OLAP (Online Analytical Processing) MCQs

Welcome to MCQss.com's OLAP (Online Analytical Processing) MCQs page. Here, you will find a wide range of multiple-choice questions designed to assess your understanding of OLAP, a powerful technology used in data analysis and reporting.

OLAP, or Online Analytical Processing, is a methodology that enables users to perform complex and multidimensional analysis of large datasets. It provides a flexible and interactive way to explore data from various perspectives, allowing users to drill down, roll up, slice, and dice data for deeper insights.

These MCQs cover different aspects of OLAP, including its concepts, architectures, data modeling techniques, and query operations. You will have the opportunity to test your knowledge of OLAP dimensions, hierarchies, measures, cubes, and the various types of OLAP operations, such as slicing, dicing, pivoting, and drilling.

By engaging with these MCQs, you can assess your proficiency in OLAP and enhance your understanding of its capabilities. Whether you are a student, a data analyst, a business intelligence professional, or an aspiring data scientist, these MCQs will help you solidify your knowledge and skills in OLAP.

Mastering OLAP is crucial for effectively analyzing and visualizing complex datasets, enabling informed decision-making within organizations. By honing your expertise through these MCQs, you will gain the necessary knowledge to leverage OLAP technology and derive valuable insights from data.

1: Which type of data models are used by databases configured for OLAP?

A.   Single dimensional

B.   Two dimensional

C.   Three dimensional

D.   Multidimensional

2: What is the other name for OLAP cube?

A.   Cube

B.   Multidimensional cube And Hyper cube

C.   Hyper cube

D.   Multidimensional cube

3: What are the applications of OLAP?

A.   None

B.   Business reporting for sales, marketing

C.   Budgeting

D.   Forecasting

4: Following are three steps in random order for creating a data cube. 1. Chose a data source 2. Create the query that extracts data from the database 3. Create the cube from the extracted data

A.   3,2,1

B.   1,2,3

C.   3,1,2

D.   2,1,3

5: What is sparsity?

A.   A condition when each cell of the cube is not filled with data.

B.   A condition when each cell of the cube is not filled with data and that leads to less processing time.

C.   A condition when each cell of the cube is filled with data and that leads to more processing time.

D.   A condition when each cell of the cube is not filled with data and that leads to more processing time.

6: What is the source of the OLAP cube’s metadata?

A.   Star schema

B.   Star schema And Snow flake schema

C.   Database

D.   Snow flake schema

7: Can the user perform data-entry or editing tasks on OLAP data?

A.   No

B.   Yes

8: __________operation of OLAP provides alternate presentation of data by rotating it.

A.   Dice

B.   Slice

C.   Pivot

D.   Roll up

9: _______________operation of OLAP involves computing all of the data relationships for one or more dimensions.

A.   Pivot

B.   Dice

C.   Slice

D.   Roll up

10: The output of an OLAP query is displayed as

A.   Pivot

B.   Matrix

C.   Excel

D.   Matrix And Pivot

11: Which of the following below is/are OLAP vendors?

A.   Cognos

B.   Infor

C.   Oracle corporation

D.   All

12: Aggregations are built from the _________________ by changing the granularity on specific dimensions.

A.   Fact table

B.   Schema

C.   None

D.   Dimension table

13: MOLAP databases generally give better performance.

A.   True

B.   False

14: It is because of ________________ that enables OLAP to achieve great performance for a query.

A.   Composition

B.   Dice

C.   Aggregation

D.   Hybrid

15: Dice operation is also known as rotate.

A.   False

B.   True

16: A key feature of online analytical processing is the ability to ________.

A.   Redict

B.   Drill down

C.   Confound

D.   Explain

E.   Retract