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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 4 of 10

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Q31.
What is a challenge when dealing with rapidly changing dimensions?

a.

Creating new dimension tables for each change

b.

Handling low cardinality attributes

c.

Managing a flat dimension table

d.

Littering the dimension table with many additional rows

Discuss
Answer: (d).Littering the dimension table with many additional rows Explanation:Rapidly changing dimensions can pose challenges by filling the dimension table with a large number of additional rows every time there is an incremental load.
Q32.
When is the type 2 approach feasible for handling rapidly changing dimensions?

a.

When dimension attributes are kept flat

b.

When additional rows are created for each change

c.

When the number of rows in the dimension is small

d.

When the dimension table is fully denormalized

Discuss
Answer: (a).When dimension attributes are kept flat Explanation:The type 2 approach is feasible for rapidly changing dimensions when dimension attributes are kept flat.
Q33.
How is the existence of multiple rows for the same customer in a rapidly changing dimension noticeable to end-users?

a.

It is always visible in all queries.

b.

It is hidden in all queries.

c.

It is practically hidden for most queries.

d.

It causes errors in query results.

Discuss
Answer: (c).It is practically hidden for most queries. Explanation:The existence of multiple rows for the same customer in a rapidly changing dimension is practically hidden for most queries.
Q34.
What is one effective approach for handling large, rapidly changing dimensions in a data warehouse?

a.

Creating additional rows for each change

b.

Separating rapidly changing attributes into another dimension table

c.

Keeping the dimension table flat

d.

Normalizing the dimension table

Discuss
Answer: (b).Separating rapidly changing attributes into another dimension table Explanation:An effective approach for handling large, rapidly changing dimensions is to separate rapidly changing attributes into another dimension table.
Q35.
What are "junk dimensions" in the context of data warehousing?

a.

Unusable fields in source data structures

b.

Dimension tables that contain only flags and textual data

c.

Fields that need to be discarded from the data warehouse

d.

Dimension tables used for constraining queries based on flag/text values

Discuss
Answer: (d).Dimension tables used for constraining queries based on flag/text values Explanation:"Junk dimensions" in data warehousing are dimension tables used for constraining queries based on flag/text values.
Q36.
Why is excluding and discarding all flags and textual data from dimension tables not a good option?

a.

It simplifies the data structure.

b.

It reduces the size of the fact table.

c.

Valuable information may be lost.

d.

It improves query performance.

Discuss
Answer: (c).Valuable information may be lost. Explanation:Excluding and discarding all flags and textual data is not a good option because valuable information may be lost.
Q37.
What is the result of completely normalizing all dimension tables in a STAR schema?

a.

A snowflake schema

b.

A denormalized schema

c.

A fact table with minimal dimensions

d.

A hybrid schema

Discuss
Answer: (a).A snowflake schema Explanation:Completely normalizing all dimension tables in a STAR schema results in a snowflake schema.
Q38.
In a snowflake schema, how does the structure of dimension tables compare to a classic STAR schema?

a.

Dimension tables are denormalized for optimal query access.

b.

Dimension tables are in the third normal form.

c.

Dimension tables are flattened.

d.

Dimension tables are kept in their original form.

Discuss
Answer: (b).Dimension tables are in the third normal form. Explanation:In a snowflake schema, dimension tables are typically normalized and in the third normal form.
Q39.
What is the primary benefit of using a snowflake schema in data warehousing?

a.

Improved query performance

b.

Simplified data modeling

c.

Reduced dimension table size

d.

Enhanced fact table design

Discuss
Answer: (a).Improved query performance Explanation:The primary benefit of using a snowflake schema is improved query performance.
Q40.
In a classic STAR schema, where is the fact table typically located in relation to dimension tables?

a.

In the middle

b.

On the left side

c.

On the right side

d.

Above the dimension tables

Discuss
Answer: (a).In the middle Explanation:In a classic STAR schema, the fact table is typically located in the middle, surrounded by dimension tables.
Page 4 of 10

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