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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 1 of 10

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Q1.
What is the primary structure of the STAR schema?

a.

Fact table with dimension tables

b.

Fact table with another fact table

c.

Dimension tables with no fact table

d.

Fact table with only one dimension table

Discuss
Answer: (a).Fact table with dimension tables Explanation:The STAR schema consists of a fact table in the middle surrounded by dimension tables.
Q2.
What is the relationship between dimension tables and the fact table in the STAR schema?

a.

One-to-one relationship

b.

Many-to-one relationship

c.

Many-to-many relationship

d.

No relationship

Discuss
Answer: (b).Many-to-one relationship Explanation:Each dimension table has a many-to-one relationship with the fact table, with the primary key of each dimension table serving as a foreign key in the fact table.
Q3.
What advantages does the STAR schema offer in decision support systems?

a.

Complexity for users

b.

Optimized data navigation

c.

Suitable for transactional environments

d.

Redundancy and inefficiency

Discuss
Answer: (b).Optimized data navigation Explanation:The STAR schema is advantageous for decision support systems as it optimizes data navigation and is suitable for query-centric environments.
Q4.
In what ways can dimension tables be explored in the STAR schema?

a.

Aggregating metrics

b.

Storing aggregate numbers in fact tables

c.

Normalizing the design

d.

Avoiding redundancy

Discuss
Answer: (a).Aggregating metrics Explanation:Dimension tables can be explored by aggregating metrics and storing aggregate numbers in additional fact tables.
Q5.
What is a key characteristic of the STAR schema design?

a.

Highly normalized

b.

Denormalized

c.

Low redundancy

d.

Efficient for OLTP systems

Discuss
Answer: (b).Denormalized Explanation:The STAR schema is a denormalized design.
Q6.
How do dimension tables change over time in a data warehouse?

a.

They grow in size with new rows.

b.

They are updated with changes.

c.

They are constant over time.

d.

They are not used in a data warehouse.

Discuss
Answer: (a).They grow in size with new rows. Explanation:Dimension tables can change over time by growing in size with new rows and by being updated with changes to their attributes.
Q7.
What term is used to describe changes to dimension tables where attributes are overwritten with new values?

a.

Type 1 changes

b.

Type 2 changes

c.

Type 3 changes

d.

Normalization

Discuss
Answer: (a).Type 1 changes Explanation:Type 1 changes involve overwriting dimension table attributes with new values.
Q8.
When is overwriting of dimension table attributes considered appropriate in a data warehouse?

a.

Always

b.

Never

c.

It depends on the type of change and information preservation.

d.

Only for dimension tables with few attributes

Discuss
Answer: (c).It depends on the type of change and information preservation. Explanation:Overwriting of dimension table attributes is not always the appropriate option in a data warehouse, and it depends on the type of change and what information must be preserved.
Q9.
What are the three types of dimension table changes in data warehousing?

a.

Type A, Type B, Type C

b.

Type I, Type II, Type III

c.

Fast Change, Slow Change, No Change

d.

Dimensional Change, Fact Change, Measure Change

Discuss
Answer: (b).Type I, Type II, Type III Explanation:The three types of dimension table changes in data warehousing are Type I, Type II, and Type III changes.
Q10.
What type of changes involve preserving historical data in dimension tables?

a.

Type 1 changes

b.

Type 2 changes

c.

Type 3 changes

d.

All types of changes

Discuss
Answer: (b).Type 2 changes Explanation:Type 2 changes involve preserving historical data in dimension tables.
Page 1 of 10

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