Exam 9: Data Warehousing

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An enterprise data warehouse is the control point and single source of all data made available to end users for decision support applications.

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Periodic data are data that are never physically altered or deleted once they have been added to the store.

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When would it be appropriate to use factless fact tables?

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There are some situations where we would want to just keep keys to dimensions but no facts. One case would be a data warehouse to keep track of events, such as attendance in a course. Another case would be coverage, taking inventory of the set of possible occurrences.

Which of the following organizational trends does not encourage the need for data warehousing?

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Advances in computer hardware, particularly the emergence of affordable mass storage and parallel computer architectures, was one of the key advances that led to the emergence of data warehousing.

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Medical claims and pharmaceutical data would be an example of big data.

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The enterprise data model controls the phased evolution of the data warehouse.

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A fact table holds descriptive data about the business.

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All of the following are ways to handle changing dimensions EXCEPT:

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The first requirement for building a user-friendly interface is a set of metadata that describes the data in the data mart in business terms that users can easily understand.

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Which of the following is true of data visualization?

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A logical data mart is a(n):

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Every key used to join the fact table with a dimension table should be a ________ key.

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The real-time data warehouse is characterized by which of the following?

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Discuss the history of data warehousing.

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________ is an ill-defined term applied to databases where size strains the ability of commonly used relational DBMSs to manage the data.

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Explain the dependent data mart and operational data store architecture.

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An independent data mart is filled with data extracted from the operational environment without the benefit of a data warehouse.

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Logical data marts are physically separate databases from the enterprise data warehouse.

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What are some approaches to handling slowly changing dimensions?

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