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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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