Q. What is the primary advantage of using core and custom tables in a data warehouse for businesses with diverse products and services?

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Q. In a manufacturing business, what are the typical steps through which a product travels before becoming finished goods?

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Q. What is the primary purpose of creating STAR schemas for different steps in a value chain in a business?

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Q. In a healthcare institution, what kind of structure is often observed when adding value to patient services?

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Q. What are some units that may be part of the value circle of a large health maintenance organization?

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

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Q. What is a key requirement for dimensions to be considered conformed in a data warehouse?

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Q. What is one of the key requirements for standardizing facts in data warehousing?

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Q. Which of the following is an example of a type of fact that needs to be standardized?

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Q. What is the purpose of using conformed dimensions in a data warehouse?

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Q. What is a key consideration for standardizing facts in data warehousing?

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Q. Which type of dimension change involves corrections to the data in a data warehouse?

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Q. Type 1 changes for slowly changing dimensions relate to correction of errors.

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Q. To apply type 3 changes of slowly changing dimensions, overwrite the attribute value in the dimension table row with the new value.

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Q. Large dimensions usually have multiple hierarchies.

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Q. The STAR schema is a normalized version of the snowflake schema.

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Q. Aggregates are precalculated summaries.

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Q. The percentage of sparsity of the base table tends to be higher than that of aggregate tables.

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Q. The fact tables of the STARS in a family share dimension tables.

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Q. Core and custom fact tables are useful for companies with several lines of service.

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