adplus-dvertising
frame-decoration

Question

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

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.

Engage with the Community - Add Your Comment

Confused About the Answer? Ask for Details Here.

Know the Explanation? Add it Here.

Q. How do Type 3 changes enable tracking forward and backward?

Similar Questions

Discover Related MCQs

Q. What is the method for applying Type 3 changes to the data warehouse?

Q. What is the key characteristic of Type 3 changes in dimension tables?

Q. What is one of the considerations when dealing with large dimensions in a data warehouse?

Q. What can be a characteristic of a large dimension in a data warehouse?

Q. Which dimension is expected to be gigantic for enterprises dealing with the general public?

Q. What is a challenge when dealing with large dimensions in a data warehouse?

Q. What is a common characteristic of large dimensions in data warehousing?

Q. How do multiple hierarchies within a dimension impact users in different departments?

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

Q. When is the type 2 approach feasible for handling rapidly changing dimensions?

Q. How is the existence of multiple rows for the same customer in a rapidly changing dimension noticeable to end-users?

Q. What is one effective approach for handling large, rapidly changing dimensions in a data warehouse?

Q. What are "junk dimensions" in the context of data warehousing?

Q. Why is excluding and discarding all flags and textual data from dimension tables not a good option?

Q. What is the result of completely normalizing all dimension tables in a STAR schema?

Q. In a snowflake schema, how does the structure of dimension tables compare to a classic STAR schema?

Q. What is the primary benefit of using a snowflake schema in data warehousing?

Q. In a classic STAR schema, where is the fact table typically located in relation to dimension tables?

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

Q. When a user runs a query constraining only on product category, what is the advantage of indexing the product dimension table on product category?