adplus-dvertising
frame-decoration

Question

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

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.

Engage with the Community - Add Your Comment

Confused About the Answer? Ask for Details Here.

Know the Explanation? Add it Here.

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

Similar Questions

Discover Related MCQs

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?

Q. What is the primary reason for snowflaking dimension tables in a data warehouse?

Q. What is the typical net storage space savings achieved by snowflaking when the dimension table contains long text fields?

Q. What is one of the disadvantages of snowflaking in a data warehouse?

Q. In a data warehouse environment, what takes the highest significance, making snowflaking generally not recommended?

Q. In data warehousing, what is the primary principle behind snowflaking?

Q. When forming subdimensions in a data warehouse, what is one valid reason for separating out specific attributes into another table?

Q. What are aggregate fact tables in data warehousing?

Q. In data warehousing, what is the primary difference between queries run in an operational system and those run in a data warehouse environment?

Q. Which of the following queries would likely run the fastest in a data warehouse without using aggregate fact tables?

Q. When might aggregate fact tables be most helpful in improving query performance in a data warehouse?

Q. In a typical data warehouse, what is the main reason for needing detailed data at the lowest level of granularity in the base fact tables?

Q. In a grocery chain data warehouse with 300 stores, 40,000 products, and daily sales data, what is the maximum number of base fact table records at the lowest level of granularity?

Q. When running queries in a data warehouse environment, which type of result sets do users typically need?