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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 5 of 10

Explore more Topics under Data Warehousing and OLAP

Q41.
In a data warehouse, why might you consider partially or fully normalizing dimension tables?

a.

To simplify the schema structure

b.

To improve query performance

c.

To save storage space

d.

To enhance the fact table design

Discuss
Answer: (b).To improve query performance Explanation:Partially or fully normalizing dimension tables in a data warehouse can be done to improve query performance.
Q42.
When a user runs a query constraining only on product category, what is the advantage of indexing the product dimension table on product category?

a.

Faster query performance

b.

Reduced storage space

c.

Improved schema simplicity

d.

Fewer joins in the query

Discuss
Answer: (a).Faster query performance Explanation:Indexing the product dimension table on product category leads to faster query performance.
Q43.
What is the primary reason for snowflaking dimension tables in a data warehouse?

a.

Significant savings in storage space

b.

Improved query performance

c.

Simplified data modeling

d.

Enhanced dimension table design

Discuss
Answer: (a).Significant savings in storage space Explanation:The primary reason for snowflaking dimension tables is to save storage space.
Q44.
What is the typical net storage space savings achieved by snowflaking when the dimension table contains long text fields?

a.

Approximately 500,000 times the size of the text fields

b.

A small percentage of the original storage space

c.

A significant reduction in storage space

d.

Double the original storage space

Discuss
Answer: (b).A small percentage of the original storage space Explanation:The typical net storage space savings achieved by snowflaking is a small percentage of the original storage space.
Q45.
What is one of the disadvantages of snowflaking in a data warehouse?

a.

Enhanced query performance

b.

Improved end-user satisfaction

c.

Schema complexity

d.

Reduced update and maintenance efforts

Discuss
Answer: (c).Schema complexity Explanation:One of the disadvantages of snowflaking is schema complexity, which can be off-putting to end-users.
Q46.
In a data warehouse environment, what takes the highest significance, making snowflaking generally not recommended?

a.

Storage space savings

b.

Schema simplicity

c.

Query performance

d.

Normalized structures

Discuss
Answer: (c).Query performance Explanation:In a data warehouse environment, query performance takes the highest significance, and snowflaking can degrade query performance.
Q47.
In data warehousing, what is the primary principle behind snowflaking?

a.

Normalizing fact tables

b.

Denormalizing dimension tables

c.

Normalizing dimension tables

d.

Simplifying the schema structure

Discuss
Answer: (c).Normalizing dimension tables Explanation:The primary principle behind snowflaking is normalizing dimension tables by removing low cardinality attributes and forming separate tables.
Q48.
When forming subdimensions in a data warehouse, what is one valid reason for separating out specific attributes into another table?

a.

Improved query performance

b.

Storage space savings

c.

Synchronization of data loading

d.

Enhanced granularity of data

Discuss
Answer: (b).Storage space savings Explanation:Separating out specific attributes into another table (subdimension) can lead to storage space savings, especially when data loads occur at different times.
Q49.
What are aggregate fact tables in data warehousing?

a.

Fact tables with only text data

b.

Precalculated summaries derived from the most granular fact table

c.

Fact tables with normalized data

d.

Unprocessed raw data tables

Discuss
Answer: (b).Precalculated summaries derived from the most granular fact table Explanation:Aggregate fact tables are precalculated summaries derived from the most granular fact table.
Q50.
In data warehousing, what is the primary difference between queries run in an operational system and those run in a data warehouse environment?

a.

Operational system queries are faster

b.

Data warehouse queries produce large result sets

c.

Data warehouse queries involve more data entry

d.

Operational system queries involve complex arithmetic algorithms

Discuss
Answer: (b).Data warehouse queries produce large result sets Explanation:The primary difference is that data warehouse queries produce large result sets compared to operational system queries.
Page 5 of 10

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