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M1 Quiz: Dimensional Modeling Results for Bhagavan Chava Score for this attempt: 100 out of 100 Submitted Jun 11, 2014 at 7:21pm This attempt took 3 minutes. 10 / 10 pts Question 1 Even though fact tables may be very large, they are often sparse since not every combination of dimensions is represented. true Correct! false
10 / 10 pts Question 2 Dimension tables often have many attributes to provide a rich query environment.
10 / 10 pts Question 3 Data warehouses are analysis oriented and typically read­only or read­mostly databases.
10 / 10 pts Question 4 The denormalization of dimensions can simplify the model for users, as well as making simpler join paths for the query optimizer.
10 / 10 pts Question 5 It is common to use geographic information as a dimension in a data warehouse. true Correct! false
10 / 10 pts Question 6 Fact tables (at the center of a star schema) are often very large, accumulating fairly detailed information about real­world events.
10 / 10 pts Question 7 It is very rare for a data warehouse to include an explicit date dimension.
10 / 10 pts Question 8 Dimensional models in a relational database consist of completely denormalized fact and dimension tables.
10 / 10 pts Question 9 One of the key issues in data warehouse design is finding the right level of granularity or detail for the foundation tables. true Correct! false
10 / 10 pts Question 10 Designing data warehouses involves dimensional modeling and a focus on data cubes.
Quiz Score: 100 out of 100
M1 Quiz: Dimensional Modeling Results for Bhagavan Chava Score for this attempt: 100 out of 100 Submitted Jun 11, 2014 at 7:40pm This attempt took 7 minutes. 10 / 10 pts Question 1 You (as a data warehouse designer) should use surrogate or artificial keys for dimension tables to protect against changes in the source systems, as well as to allow for handling changes over time.

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