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 6 of 8

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

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

Q51.

Why do data warehouses feed data into data mining applications?

To reduce storage costs

To support summary queries

To improve query performance

To provide details at the lowest granularity

Discuss

Answer: (d).To provide details at the lowest granularity Explanation:Data warehouses feed data into data mining applications to provide details at the lowest granularity for advanced data analysis.

Q52.

In a dimension table, what is the primary key used for?

Identifying the production system key

Maintaining built-in meanings

Uniquely identifying each row

Representing operational system data

Discuss

Answer: (c).Uniquely identifying each row Explanation:The primary key in a dimension table is used to uniquely identify each row in the table.

Q53.

What issues can arise when using operational system keys as primary keys for dimension tables?

Data redundancy and inconsistency

Compatibility with data warehousing tools

Improved query performance

Efficient data aggregation

Discuss

Answer: (a).Data redundancy and inconsistency Explanation:Using operational system keys as primary keys can lead to data redundancy and inconsistency, especially when operational keys change or have built-in meanings.

Q54.

What is the first principle to consider when choosing primary keys for dimension tables?

Maintain built-in meanings in keys

Avoid using surrogate keys

Avoid using production system keys

Prioritize query performance

Discuss

Answer: (c).Avoid using production system keys Explanation:The first principle is to avoid using production system keys as primary keys for dimension tables.

Q55.

What are surrogate keys in dimension tables?

Keys with built-in meanings

Keys mapped directly to operational system keys

System-generated sequence numbers

Keys primarily used for query performance

Discuss

Answer: (c).System-generated sequence numbers Explanation:Surrogate keys are system-generated sequence numbers without built-in meanings.

Q56.

How are operational system keys typically handled in dimension tables when surrogate keys are used?

Operational system keys are not allowed in dimension tables.

Operational system keys are used as the primary key.

Operational system keys are mapped to surrogate keys and kept as attributes.

Operational system keys are replaced with primary keys.

Discuss

Answer: (c).Operational system keys are mapped to surrogate keys and kept as attributes. Explanation:Operational system keys are mapped to surrogate keys and typically kept as attributes in dimension tables.

Q57.

Why do dimension tables often use surrogate keys?

To maintain built-in meanings in the keys

To simplify query performance

To eliminate the need for operational system keys

To avoid issues related to operational key changes and reassignments

Discuss

Answer: (d).To avoid issues related to operational key changes and reassignments Explanation:Dimension tables often use surrogate keys to avoid issues related to operational key changes and reassignments.

Q58.

In a STAR schema, why must the primary key of each dimension table also be a foreign key in the fact table?

To increase the size of the fact table

To complicate the structure of the fact table

To enforce data consistency

To enable one-to-one relationships between tables

Discuss

Answer: (c).To enforce data consistency Explanation:Foreign keys in the fact table ensure data consistency and maintain the relationships between dimension and fact tables.

Q59.

Which option is commonly used for defining primary keys in a STAR schema fact table?

A single compound primary key with additional attributes

A concatenated primary key without additional attributes

A generated primary key with additional attributes

A generated primary key without additional attributes

Discuss

Answer: (b).A concatenated primary key without additional attributes Explanation:In practice, option b is commonly used for defining primary keys in a STAR schema fact table.

Q60.

What is the fundamental difference between the STAR schema and relational schemas for OLTP systems?

STAR schema is designed for data warehousing, while relational schemas are for OLTP systems.

STAR schema is heavily normalized, while relational schemas are denormalized.

STAR schema includes foreign keys, while relational schemas do not.

STAR schema uses compound primary keys, while relational schemas use concatenated keys.

Discuss

Answer: (a).STAR schema is designed for data warehousing, while relational schemas are for OLTP systems. Explanation:The STAR schema is designed for data warehousing, while relational schemas are typically used for OLTP systems.

Page 6 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 6 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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