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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 3 of 10

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Q21.
What is the characteristic of Type 3 changes related to tracking orders through transitions?

a.

Preservation of old values only

b.

Preservation of new values only

c.

Preservation of both old and new values

d.

Deletion of all values

Discuss
Answer: (c).Preservation of both old and new values Explanation:Type 3 changes involve the need to keep track of history with both old and new values of the changed attribute.
Q22.
How do Type 3 changes enable tracking forward and backward?

a.

By deleting old values

b.

By creating new dimension tables

c.

By using effective date ranges

d.

By preserving both old and new values

Discuss
Answer: (d).By preserving both old and new values Explanation:Type 3 changes enable tracking forward and backward by preserving both old and new values of changed attributes.
Q23.
What is the method for applying Type 3 changes to the data warehouse?

a.

Overwrite the attribute value in the dimension table row with the new value

b.

Add a new dimension table row with the new value of the changed attribute

c.

Delete the dimension table row

d.

Create a new dimension table for each change

Discuss
Answer: (b).Add a new dimension table row with the new value of the changed attribute Explanation:The method for applying Type 3 changes to the data warehouse is to add a new dimension table row with the new value of the changed attribute.
Q24.
What is the key characteristic of Type 3 changes in dimension tables?

a.

Preservation of historical data

b.

Correction of errors

c.

Handling of tentative or soft changes

d.

Changes with no significance

Discuss
Answer: (c).Handling of tentative or soft changes Explanation:Type 3 changes in dimension tables are characterized by handling tentative or soft changes.
Q25.
What is one of the considerations when dealing with large dimensions in a data warehouse?

a.

They don't require special handling.

b.

They typically have a low cardinality of attributes.

c.

Populating large-dimension tables is always fast and efficient.

d.

Efficient design, proper indexes, and optimizing techniques are needed.

Discuss
Answer: (d).Efficient design, proper indexes, and optimizing techniques are needed. Explanation:Dealing with large dimensions in a data warehouse often requires efficient design, proper indexes, and optimizing techniques.
Q26.
What can be a characteristic of a large dimension in a data warehouse?

a.

Limited number of rows

b.

Limited number of dimension attributes

c.

Multiple hierarchies

d.

Fast and efficient query performance

Discuss
Answer: (c).Multiple hierarchies Explanation:A large dimension in a data warehouse can have multiple hierarchies among its dimension attributes.
Q27.
Which dimension is expected to be gigantic for enterprises dealing with the general public?

a.

Product dimension

b.

Time dimension

c.

Customer dimension

d.

Geography dimension

Discuss
Answer: (c).Customer dimension Explanation:The customer dimension is expected to be gigantic for enterprises dealing with the general public.
Q28.
What is a challenge when dealing with large dimensions in a data warehouse?

a.

Speed and efficiency are not important.

b.

There is no need for proper indexing.

c.

Query performance can be slow and inefficient.

d.

Large dimensions have low cardinality.

Discuss
Answer: (c).Query performance can be slow and inefficient. Explanation:Query performance for large dimensions in a data warehouse can be slow and inefficient.
Q29.
What is a common characteristic of large dimensions in data warehousing?

a.

Low cardinality of attributes

b.

Rapid changes in dimension values

c.

Multiple hierarchies

d.

Fast and efficient query performance

Discuss
Answer: (c).Multiple hierarchies Explanation:Large dimensions in data warehousing often possess multiple hierarchies.
Q30.
How do multiple hierarchies within a dimension impact users in different departments?

a.

They simplify the dimension structure for all users.

b.

Each department uses the same set of attributes.

c.

Users from different departments use different attribute hierarchies.

d.

Hierarchies do not affect user interactions with the dimension.

Discuss
Answer: (c).Users from different departments use different attribute hierarchies. Explanation:Multiple hierarchies within a dimension allow users from different departments to use different attribute hierarchies for their specific needs.
Page 3 of 10

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