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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.

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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 7 of 8

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Q61.
When might a denormalized dimension table not be desirable in a STAR schema?

a.

When the data warehouse has a large number of customers

b.

When query performance is a top priority

c.

When the fact table is small in size

d.

When using concatenated primary keys

Discuss
Answer: (a).When the data warehouse has a large number of customers Explanation:A denormalized dimension table may not be desirable in a STAR schema when the data warehouse has a large number of customers, as it can increase the size of the fact table.
Q62.
What is one of the key advantages of the STAR schema for data warehousing?

a.

High degree of normalization for efficient storage

b.

Complex multi-table joins for better query performance

c.

Ease of query performance optimization

d.

Relational schemas with fully normalized tables

Discuss
Answer: (c).Ease of query performance optimization Explanation:One of the key advantages of the STAR schema for data warehousing is its ease of query performance optimization.
Q63.
In the context of a STAR schema, what does "denormalized" refer to?

a.

Redundancy in data storage

b.

High data consistency

c.

Minimal data storage requirements

d.

Low query performance

Discuss
Answer: (a).Redundancy in data storage Explanation:In the context of a STAR schema, "denormalized" refers to the intentional inclusion of some data redundancy for better query performance.
Q64.
In a data warehouse, what distinguishes users of decision support systems, such as data warehouses, from users of OLTP systems?

a.

Decision support system users need to interact through GUI screens.

b.

Decision support system users must understand data structures.

c.

Users of OLTP systems must formulate queries.

d.

Users of OLTP systems need IT professionals to interact with the data.

Discuss
Answer: (b).Decision support system users must understand data structures. Explanation:Users of decision support systems need to understand data structures and how data is associated within the data warehouse.
Q65.
How does the STAR schema differ from the relational schema of an OLTP system in terms of user-friendliness?

a.

STAR schema is more normalized, making it easier for users to query.

b.

STAR schema has complex join paths, which can be confusing for users.

c.

STAR schema aligns with how users think and need data for querying.

d.

STAR schema requires users to pass through multiple tables for basic results.

Discuss
Answer: (c).STAR schema aligns with how users think and need data for querying. Explanation:The STAR schema aligns with how users think and need data for querying, making it more user-friendly.
Q66.
Why is the STAR schema considered to be intuitively understood by users?

a.

It contains highly normalized tables.

b.

It uses complex join paths.

c.

It aligns with the way users visualize relationships.

d.

It requires extensive training for users to comprehend.

Discuss
Answer: (c).It aligns with the way users visualize relationships. Explanation:The STAR schema aligns with the way users normally visualize relationships and, therefore, is intuitively understood by them.
Q67.
How does the STAR schema optimize navigation in the database?

a.

It increases the number of join paths, making navigation complex.

b.

It uses convoluted relationships to slow down navigation.

c.

It simplifies join paths, making navigation straightforward.

d.

It focuses on building highly normalized tables for precision.

Discuss
Answer: (c).It simplifies join paths, making navigation straightforward. Explanation:The STAR schema simplifies join paths, making navigation straightforward and optimizing the user's ability to access data.
Q68.
In the context of a STAR schema, what happens when you have a seemingly complex query to resolve a specific problem?

a.

Navigation becomes difficult and slow.

b.

Navigation remains simple and straightforward.

c.

Users need to pass through multiple tables for the result.

d.

Query results may not be accurate.

Discuss
Answer: (b).Navigation remains simple and straightforward. Explanation:Even with seemingly complex queries, navigation in a STAR schema remains simple and straightforward.
Q69.
Why is the STAR schema considered most suitable for query processing in a data warehouse?

a.

It uses complex join paths to enhance data exploration.

b.

It increases the number of dimensions to speed up queries.

c.

It simplifies join paths, making query processing straightforward.

d.

It requires no joins between fact and dimension tables.

Discuss
Answer: (c).It simplifies join paths, making query processing straightforward. Explanation:The STAR schema simplifies join paths, making query processing straightforward and efficient.
Q70.
In a three-dimensional query using the STAR schema, what is the typical sequence of operations to obtain the final result?

a.

Select rows from fact table, then from dimension tables based on query parameters.

b.

Select rows from dimension tables based on query parameters, then from the fact table.

c.

Start with the fact table and perform joins with dimension tables.

d.

Roll up the dimensions before querying the fact table.

Discuss
Answer: (b).Select rows from dimension tables based on query parameters, then from the fact table. Explanation:In a STAR schema, the typical sequence is to select rows from dimension tables based on query parameters and then from the fact table.
Page 7 of 8

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