Welcome to the Principles of Dimensional Modeling MCQs Page

Dive deep into the fascinating world of Principles of Dimensional Modeling with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Principles of 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 Principles of 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 Principles of 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 Principles of Dimensional Modeling. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Principles of Dimensional Modeling MCQs | Page 5 of 8

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01

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02

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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

Advanced Topics in 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

Q41.

What is the characteristic of fully additive measures in a fact table?

They are non-numeric values

They cannot be aggregated

Aggregation is done by simple addition

They are always calculated metrics

Discuss

Answer: (c).Aggregation is done by simple addition Explanation:Fully additive measures in a fact table can be aggregated by simple addition.

Q42.

Which attribute in a fact table is an example of a semiadditive measure?

Quantity ordered

Margin dollars

Extended cost

Order dollars

Discuss

Answer: (b).Margin dollars Explanation:Margin dollars, which are derived from other measures, are an example of semiadditive measures because they cannot be directly summed up.

Q43.

How does the depth and width of a fact table typically compare to a dimension table?

The fact table is wider and deeper

The fact table is narrower and deeper

The fact table has more attributes

The fact table is always narrower and shallower

Discuss

Answer: (b).The fact table is narrower and deeper Explanation:Fact tables are typically narrower (fewer attributes) but deeper (more rows) compared to dimension tables.

Q44.

What is the term used to describe the situation when some rows in a fact table have null measures?

Dense data

Narrow data

Deep data

Sparse data

Discuss

Answer: (d).Sparse data Explanation:Sparse data refers to the situation where some rows in a fact table have null or missing measures.

Q45.

What are attributes like order number and order line, which are neither facts nor dimension attributes, called in a fact table?

Degenerate dimensions

Dense attributes

Primary keys

Derived measures

Discuss

Answer: (a).Degenerate dimensions Explanation:Attributes like order number and order line, which are not facts or dimension attributes, are referred to as degenerate dimensions in a fact table.

Q46.

What is a factless fact table?

A fact table with missing data

A fact table that contains only the number one

A fact table that lacks primary keys

A fact table that doesn't need dimension tables

Discuss

Answer: (b).A fact table that contains only the number one Explanation:A factless fact table contains rows where the presence of a corresponding row itself represents an event, often indicated by the number one.

Q47.

In a factless fact table representing student attendance, what value is typically recorded in each row to indicate attendance?

The student's name

The date of attendance

The number one

The course name

Discuss

Answer: (c).The number one Explanation:In a factless fact table for student attendance, the number one is often recorded in each row to indicate attendance.

Q48.

What is one advantage of keeping the fact table at the lowest granularity level?

Improved query performance

Reduced storage requirements

Simplicity in data modeling

Faster data extraction from operational systems

Discuss

Answer: (a).Improved query performance Explanation:Keeping the fact table at the lowest granularity level allows for efficient drill-down and roll-up operations, leading to improved query performance.

Q49.

What are the natural lowest levels of dimensions in a fact table?

The most detailed and specific attributes

The attributes with the highest cardinality

The attributes that are most frequently used

The attributes with the lowest cardinality

Discuss

Answer: (a).The most detailed and specific attributes Explanation:The natural lowest levels of dimensions in a fact table are the most detailed and specific attributes, such as individual product, date, customer, and sales representative.

Q50.

How do granular fact tables facilitate "graceful" changes to data models?

By requiring changes in existing queries

By necessitating changes in the fact table structure

By allowing new attributes to be added without affecting old queries

By minimizing the number of fact table rows

Discuss

Answer: (c).By allowing new attributes to be added without affecting old queries Explanation:Granular fact tables facilitate "graceful" changes because new attributes can be added to dimensions without affecting old queries.

Page 5 of 8

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 🔒

OLAP in the Data Warehouse 🔒

Data Warehousing and the Web 🔒

Data Mining Basics 🔒

The Physical Design Process 🔒

Data Warehouse Deployment 🔒

Welcome to the Principles of Dimensional Modeling MCQs Page

Principles of Dimensional Modeling MCQs | Page 5 of 8

Explore more Topics under Data Warehousing and OLAP

The Compelling Need for Data Warehousing

Defining the Business Requirements

Importance of Data Quality

Matching Information to the Classes of Users

Growth and Maintenance

Important Terms and Definitions

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