Welcome to the OLAP in the Data Warehouse MCQs Page

Dive deep into the fascinating world of OLAP in the Data Warehouse with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of OLAP in the Data Warehouse, a crucial aspect of Data Warehousing and OLAP. In this section, you will encounter a diverse range of MCQs that cover various aspects of OLAP in the Data Warehouse, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Data Warehousing and OLAP.

Check out the MCQs below to embark on an enriching journey through OLAP in the Data Warehouse. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Data Warehousing and OLAP.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of OLAP in the Data Warehouse. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

OLAP in the Data Warehouse MCQs | Page 8 of 13

Explore more Topics under Data Warehousing and OLAP

01

The Compelling Need for Data Warehousing

02

Building Blocks of Data Warehouse 🔒

03

Trends in Data Warehousing 🔒

04

Planning and Project Management 🔒

05

Defining the Business Requirements

06

Requirements as the Driving Force for Data warehousing 🔒

07

Architectural Components 🔒

08

Infrastructure as the Foundation for Data Warehousing 🔒

09

Role of Metadata in Data Warehousing 🔒

10

Principles of Dimensional Modeling

11

Advanced Topics in Dimensional Modeling 🔒

12

Data Extraction,Transformation and Loading 🔒

13

Importance of Data Quality

14

Matching Information to the Classes of Users

15

Data Warehousing and the Web 🔒

16

Data Mining Basics 🔒

17

The Physical Design Process 🔒

18

Data Warehouse Deployment 🔒

19

Growth and Maintenance

20

Important Terms and Definitions

Q71.

In the ROLAP model, how is data stored?

As precalculated and prefabricated multidimensional data cubes

In proprietary multidimensional databases (MDDBs)

As rows and columns in a relational data model

As virtual multidimensional data cubes

Discuss

Answer: (c).As rows and columns in a relational data model Explanation:In the ROLAP model, data is stored as rows and columns in a relational data model.

Q72.

What is created in the ROLAP model to hide the storage structure and present data multidimensionally?

Precalculated cubes

Virtual cubes

Metadata layer

Proprietary multidimensional databases (MDDBs)

Discuss

Answer: (c).Metadata layer Explanation:In the ROLAP model, a metadata layer is created to hide the storage structure and present data multidimensionally.

Q73.

What does the metadata layer in the ROLAP model support?

Mapping of dimensions to relational tables

Storage of multidimensional hypercubes

Creation of virtual multidimensional data cubes

Summarizations in multidimensional databases

Discuss

Answer: (a).Mapping of dimensions to relational tables Explanation:The metadata layer in the ROLAP model supports the mapping of dimensions to relational tables.

Q74.

What is the role of the analytical server in the ROLAP model?

Storing precalculated cubes

Creating multidimensional views on the fly

Aggregating data in MDDBs

Serving as a special server for OLAP engines

Discuss

Answer: (b).Creating multidimensional views on the fly Explanation:In the ROLAP model, the analytical server in the application layer creates multidimensional views on the fly.

Q75.

What is a true characteristic of ROLAP?

Stores data in a multidimensional form

Supports precalculated cubes

Stores data in a relational form

Requires static multidimensional structures

Discuss

Answer: (c).Stores data in a relational form Explanation:A true characteristic of ROLAP is that it stores data in a relational form.

Q76.

What does local hypercubing provide as a variation of ROLAP?

A local, multidimensional view of the data

Precalculated cubes

A static multidimensional structure

Query performance

Discuss

Answer: (a).A local, multidimensional view of the data Explanation:Local hypercubing provides a small, local, multidimensional database for a user's analysis.

Q77.

In the ROLAP versus MOLAP comparison, what does MOLAP excel in?

Query performance

Query complexity

Aggregation support

Relational data storage

Discuss

Answer: (a).Query performance Explanation:MOLAP excels in query performance in the ROLAP versus MOLAP comparison.

Q78.

What consideration does the choice between ROLAP and MOLAP depend on?

Query performance only

Query complexity only

Both query performance and query complexity

Relational data storage only

Discuss

Answer: (c).Both query performance and query complexity Explanation:The choice between ROLAP and MOLAP depends on both query performance and query complexity.

Q79.

OLAP implementation considerations include two key issues related to the MOLAP model. What is the first issue?

Lack of standardization

Scalability

Client interface

Volumes of detailed data

Discuss

Answer: (a).Lack of standardization Explanation:The first issue in OLAP implementation considerations is the lack of standardization, as each vendor tool has its own client interface.

Q80.

What can highly normalized data in the data warehouse give rise to when performing complex analysis?

Lack of standardization

Scalability issues

Processing overhead

Multidimensional design

Discuss

Answer: (c).Processing overhead Explanation:Highly normalized data in the data warehouse can give rise to processing overhead when performing complex analysis.

Page 8 of 13

Building Blocks of Data Warehouse 🔒

Trends in Data Warehousing 🔒

Planning and Project Management 🔒

Requirements as the Driving Force for Data warehousing 🔒

Architectural Components 🔒

Infrastructure as the Foundation for Data Warehousing 🔒

Role of Metadata in Data Warehousing 🔒

Advanced Topics in Dimensional Modeling 🔒

Data Extraction,Transformation and Loading 🔒

Data Warehousing and the Web 🔒

Data Mining Basics 🔒

The Physical Design Process 🔒

Data Warehouse Deployment 🔒

Welcome to the OLAP in the Data Warehouse MCQs Page

OLAP in the Data Warehouse MCQs | Page 8 of 13

Explore more Topics under Data Warehousing and OLAP

The Compelling Need for Data Warehousing

Defining the Business Requirements

Principles of Dimensional Modeling

Importance of Data Quality

Matching Information to the Classes of Users

Growth and Maintenance

Important Terms and Definitions

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