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 10 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

Q91.

To apply type 3 changes of slowly changing dimensions, overwrite the attribute value in the dimension table row with the new value.

True

False

May be True or False

Can't say

Discuss

Answer: (b).False Explanation:To apply type 3 changes, new columns are added to the dimension table to store historical values, and the current value is updated.

Q92.

Large dimensions usually have multiple hierarchies.

True

False

May be True or False

Can't say

Discuss

Answer: (a).True Explanation:Large dimensions often have multiple hierarchies to provide various levels of granularity.

Q93.

The STAR schema is a normalized version of the snowflake schema.

True

False

May be True or False

Can't say

Discuss

Answer: (b).False Explanation:The STAR schema is a denormalized version, while the snowflake schema is normalized.

Q94.

Aggregates are precalculated summaries.

True

False

May be True or False

Can't say

Discuss

Answer: (a).True Explanation:Aggregates are precalculated summaries stored in the data warehouse to improve query performance.

Q95.

The percentage of sparsity of the base table tends to be higher than that of aggregate tables.

True

False

May be True or False

Can't say

Discuss

Answer: (b).False Explanation:Aggregate tables are often more sparse than base tables as they store summarized data.

Q96.

The fact tables of the STARS in a family share dimension tables.

True

False

May be True or False

Can't say

Discuss

Answer: (a).True Explanation:Fact tables in a STAR schema share common dimension tables.

Q97.

Core and custom fact tables are useful for companies with several lines of service.

True

False

May be True or False

Can't say

Discuss

Answer: (a).True Explanation:Core and custom fact tables allow flexibility in handling different lines of service in a data warehouse.

Q98.

Conforming dimensions are not absolutely necessary in a data warehouse.

True

False

May be True or False

Can't say

Discuss

Answer: (b).False Explanation:Conforming dimensions are important for consistency and integration across the data warehouse.

Q99.

A value circle usually needs a family of STARS to support the business.

True

False

May be True or False

Can't say

Discuss

Answer: (a).True Explanation:A value circle often requires multiple STAR schemas to provide comprehensive support for the business.

Page 10 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 10 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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