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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.

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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 9 of 10

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Q81.
In a healthcare institution, what kind of structure is often observed when adding value to patient services?

a.

Value chain

b.

Value circle

c.

Linear movement

d.

Supply chain

Discuss
Answer: (b).Value circle Explanation:In a healthcare institution, value is often added to patient services in a circular or value circle fashion.
Q82.
What are some units that may be part of the value circle of a large health maintenance organization?

a.

Retail stores, warehouses, and factories

b.

Hospitals, clinics, doctor's offices, and insurance companies

c.

Manufacturing plants, distribution centers, and suppliers

d.

Research and development, marketing, and customer support

Discuss
Answer: (b).Hospitals, clinics, doctor's offices, and insurance companies Explanation:The value circle of a large health maintenance organization may include units such as hospitals, clinics, doctor's offices, and insurance companies.
Q83.
What are conformed dimensions in the context of data warehousing?

a.

Dimensions that are exclusive to one fact table

b.

Dimensions that are shared between two or more fact tables

c.

Dimensions that are not properly resolved

d.

Dimensions that are unique to each data mart

Discuss
Answer: (b).Dimensions that are shared between two or more fact tables Explanation:Conformed dimensions are dimensions that are shared between two or more fact tables in data warehousing.
Q84.
What is a key requirement for dimensions to be considered conformed in a data warehouse?

a.

They must have varying meanings for different fact tables

b.

They must be used exclusively within one data mart

c.

They must reflect discrepancies and conflicts from source systems

d.

They must have the same meaning in relation to each fact table

Discuss
Answer: (d).They must have the same meaning in relation to each fact table Explanation:To be considered conformed dimensions in a data warehouse, dimensions must have the same meaning in relation to each fact table.
Q85.
What is one of the key requirements for standardizing facts in data warehousing?

a.

Using different algorithms for each fact table

b.

Allowing variations in terminology across data marts

c.

Resolving homonyms and synonyms

d.

Using different units of measurement for the same facts

Discuss
Answer: (c).Resolving homonyms and synonyms Explanation:Standardizing facts involves resolving homonyms and synonyms to ensure consistency.
Q86.
Which of the following is an example of a type of fact that needs to be standardized?

a.

Employee names

b.

Revenue

c.

Customer addresses

d.

Supplier contact information

Discuss
Answer: (b).Revenue Explanation:Revenue is an example of a type of fact that should be standardized in data warehousing.
Q87.
What is the purpose of using conformed dimensions in a data warehouse?

a.

To introduce variations in dimension definitions

b.

To create dimension tables exclusive to one fact table

c.

To ensure the same dimension definitions and terminology across data marts

d.

To resolve discrepancies and conflicts within dimensions

Discuss
Answer: (c).To ensure the same dimension definitions and terminology across data marts Explanation:Conformed dimensions are used to ensure the same dimension definitions and terminology across data marts in a data warehouse.
Q88.
What is a key consideration for standardizing facts in data warehousing?

a.

Utilizing different units of measurement for the same facts

b.

Consistent use of algorithms for derived units in each fact table

c.

Allowing variations in terminology for facts across data marts

d.

Ensuring the same fact definitions and terminology across data marts

Discuss
Answer: (b).Consistent use of algorithms for derived units in each fact table Explanation:Standardizing facts includes ensuring the consistent use of algorithms for derived units in each fact table.
Q89.
Which type of dimension change involves corrections to the data in a data warehouse?

a.

Type 1

b.

Type 2

c.

Type 3

d.

Slowly changing dimensions do not involve changes.

Discuss
Answer: (a).Type 1 Explanation:Type 1 dimension changes in a data warehouse relate to corrections in the data.
Q90.
Type 1 changes for slowly changing dimensions relate to correction of errors.

a.

True

b.

False

c.

May be True or False

d.

Can't say

Discuss
Answer: (a).True Explanation:Type 1 changes in slowly changing dimensions are typically used for correcting errors in attribute values.
Page 9 of 10

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