Q. How do attributes in a dimension table facilitate drilling down and rolling up in data analysis?

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Q. What is the typical relationship between the number of records in a dimension table and the fact table in a data warehouse?

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Q. What is the primary key of a fact table?

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Q. What does the term "data grain" refer to in the context of a fact table?

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Q. What is the characteristic of fully additive measures in a fact table?

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Q. Which attribute in a fact table is an example of a semiadditive measure?

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Q. How does the depth and width of a fact table typically compare to a dimension table?

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Q. What is the term used to describe the situation when some rows in a fact table have null measures?

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Q. What are attributes like order number and order line, which are neither facts nor dimension attributes, called in a fact table?

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Q. What is a factless fact table?

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Q. In a factless fact table representing student attendance, what value is typically recorded in each row to indicate attendance?

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Q. What is one advantage of keeping the fact table at the lowest granularity level?

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Q. What are the natural lowest levels of dimensions in a fact table?

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Q. How do granular fact tables facilitate "graceful" changes to data models?

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Q. Why do data warehouses feed data into data mining applications?

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Q. In a dimension table, what is the primary key used for?

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Q. What issues can arise when using operational system keys as primary keys for dimension tables?

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Q. What is the first principle to consider when choosing primary keys for dimension tables?

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Q. What are surrogate keys in dimension tables?

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Q. How are operational system keys typically handled in dimension tables when surrogate keys are used?

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