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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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?

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

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

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

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

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

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

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Q. What are aggregate fact tables in data warehousing?

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

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

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