Welcome to the Advanced Topics in Dimensional Modeling MCQs Page

Dive deep into the fascinating world of Advanced Topics in Dimensional Modeling with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Advanced Topics in Dimensional Modeling, a crucial aspect of Data Warehousing and OLAP. In this section, you will encounter a diverse range of MCQs that cover various aspects of Advanced Topics in Dimensional Modeling, 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 Advanced Topics in Dimensional Modeling. 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 Advanced Topics in Dimensional Modeling. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Advanced Topics in Dimensional Modeling MCQs | Page 8 of 10

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

Data Extraction,Transformation and Loading 🔒

12

Importance of Data Quality

13

Matching Information to the Classes of Users

14

OLAP in the Data Warehouse 🔒

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.

What is typically shared among the fact tables in a family of STAR schemas?

Aggregated measures

Derived dimension tables

Dimension tables

Aggregate fact tables

Discuss

Answer: (c).Dimension tables Explanation:Dimension tables are typically shared among the fact tables in a family of STAR schemas.

Q72.

In what kind of business would it make sense to have families of STAR schemas consisting of transaction and snapshot schemas?

Banking or telephone services

Manufacturing or production-type enterprises

Medical centers

Retail businesses

Discuss

Answer: (a).Banking or telephone services Explanation:In businesses like banking or telephone services, it makes sense to have families of STAR schemas consisting of transaction and snapshot schemas.

Q73.

In which type of business does it make sense to have a schema capturing transaction data that supports strategic decision making and revenue analysis?

Retail businesses

Manufacturing companies

Medical centers

Telephone companies

Discuss

Answer: (d).Telephone companies Explanation:A telephone company benefits from a transaction schema to support strategic decision making and revenue analysis.

Q74.

What kind of data analysis is primarily supported by a transaction schema?

Individual account balances

Total amounts at periodic intervals

Customer demographics

Product sales analysis

Discuss

Answer: (d).Product sales analysis Explanation:A transaction schema primarily supports data analysis related to individual transactions, such as product sales analysis.

Q75.

What kind of questions are typically addressed by a snapshot schema in a telephone company's data warehouse?

Questions about individual phone transactions

Questions about shifting and movement of account balances

Questions about customer demographics

Questions about specific products and services

Discuss

Answer: (b).Questions about shifting and movement of account balances Explanation:A snapshot schema typically addresses questions about the shifting and movement of account balances at specific intervals.

Q76.

In a bank's data warehouse, what does the core fact table typically contain?

Metrics common to all types of accounts

Customer demographics

Individual transaction amounts

Product sales data

Discuss

Answer: (a).Metrics common to all types of accounts Explanation:The core fact table in a bank's data warehouse typically contains metrics that are common to all types of accounts.

Q77.

In a family of STARS with core and custom tables, what is the purpose of the custom fact tables?

To hold metrics common to all products and services

To track individual transaction amounts

To contain metrics specific to a particular product or service

To store customer demographics

Discuss

Answer: (c).To contain metrics specific to a particular product or service Explanation:The custom fact tables in a family of STARS hold metrics that are specific to a particular product or service.

Q78.

What is the primary advantage of using core and custom tables in a data warehouse for businesses with diverse products and services?

Simplifies data storage by merging all metrics into one table

Allows for better tracking and analysis of specific products or services

Reduces the number of dimension tables required

Eliminates the need for a core fact table

Discuss

Answer: (b).Allows for better tracking and analysis of specific products or services Explanation:Core and custom tables allow for better tracking and analysis of specific products or services, which is especially beneficial for businesses with diverse offerings.

Q79.

In a manufacturing business, what are the typical steps through which a product travels before becoming finished goods?

Sales, production, distribution, and inventory

Raw materials, assembly, packaging, and distribution

Manufacturing, sales, packaging, and transportation

Procurement, manufacturing, sales, and distribution

Discuss

Answer: (b).Raw materials, assembly, packaging, and distribution Explanation:In a manufacturing business, a product typically travels through steps involving raw materials, assembly, packaging, and distribution before becoming finished goods.

Q80.

What is the primary purpose of creating STAR schemas for different steps in a value chain in a business?

To reduce the number of dimensions required

To simplify data storage

To track important metrics at different steps

To eliminate the need for data warehousing

Discuss

Answer: (c).To track important metrics at different steps Explanation:STAR schemas are created to track important metrics at different steps along the value chain in a business.

Page 8 of 10

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 🔒

Data Extraction,Transformation and Loading 🔒

OLAP in the Data Warehouse 🔒

Data Warehousing and the Web 🔒

Data Mining Basics 🔒

The Physical Design Process 🔒

Data Warehouse Deployment 🔒

Welcome to the Advanced Topics in Dimensional Modeling MCQs Page

Advanced Topics in Dimensional Modeling MCQs | Page 8 of 10

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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