Mis430 Data Warehousing Final

52 Questions
Data Quizzes & Trivia

MIS 430 FINAL data warehousing at gmu university

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Questions and Answers
  • 1. 
    ---------------- schema is a normalized version of the STAR schema in which dimension tables are partially or fully normalized. Not generally recommended because it compromises query performance and simplicity for understanding.
  • 2. 
    ------------------- schema is the arrangement of the collection of fact and dimension tables in the dimensional data model , resembling a star formation, with the fact table placed in the middle surrounded by the dimension tables. Each dimension table is in a one to many relationship with the fact table.
  • 3. 
    ----------------- normal form, data is atomic, no repeating groups, primary key.
  • 4. 
    -------------------- normal form, relates to rows in a table, no duplicate data in rows, duplicate data moved to another table, every attribute depends on the entire key.
  • 5. 
    ---------------------- normal form, relates to relationships between tables, no transitive dependency, every attribute depends on the primary key.
  • 6. 
    ------------------- looks like a table, acts like a table, not a table.-Restores intrusiveness to a database-Eases user querying-Data Security    abstracts the database implementation    allows for database changes behind the scenes as well    limit access to sensitive information
  • 7. 
    --------------- indexes, the order in which the data is physically stored.
  • 8. 
    -------------- indexes, internal table that points to actual data locations.
  • 9. 
    Database ------------- reduces data duplication, increases data integrity.
  • 10. 
    ---------------- break all composite attributes into single attributes, data is not duplicated in columns.
  • 11. 
    --------------------- attributes, not allowed in a normalized database, use another table.
  • 12. 
    --------------- attributes, not stored in the relational tables.
  • 13. 
    These characteristics describe ---------------- of a data warehouse?- 1. Source data ( comes from transactional databases or data mart)- 2. Data staging ( data gets filtered and cleaned)- 3. Data storage ( data gets stored)- 4. Information delivery (how users get the data ex report, query)
  • 14. 
    These characteristics describe -------------- of a data warehouse?- Tables are normalized- Consists of Fact and Dimension tables.- Information is read only
  • 15. 
    These characteristics describe -------------- tables?- A table in data ware house were measures are placed- Aggregation happens in this table- Many records in this table.- Numeric data
  • 16. 
    These characteristics describe -------------- tables?-These tables ask a question-Used to slice and dice the data- If numeric data use as a buffer- Has more columns or fields
  • 17. 
    ------------------ databases are normalized, they rely on referential integrity, relationships are important, use primary and foreign keys to relate to each other.
  • 18. 
    Type ------- dimension you simply change errors no history is maintained. Ex. Customer Key     FName     LName        Location          Gender        101                   john          smith        Washington          male--he moved from Washington to ChicagoFix  Customer Key    FName      LName        Location          Gender         101                  john           smith        Chicago               male
  • 19. 
    Type ----------- dimension, true changes in the source system, history must be preserved, partitions the history in the data warehouse, every change for the same attribute must be preserved. Ex. Customer Key      Fname         Lname       Location           Gender        101                       john          smith          washington        male         102                      john          smith           chicago             male
  • 20. 
    Type ------------------ dimension, tentative changes in the source system, need to keep old and new value, ability to track forward and backward.Ex. Customer Key      Fname       Lname        Location        Gender        101                    john           smith           washington    male        Customer Key    Fname       Lname        O location         N location      Gender        101                    john            smith          washington      chicago          male
  • 21. 
    These characteristics describe --------------------?- Family of STARS-Must have conformed dimensions-Must share 2 dimensions-Conformed fact tables -Snow flaking ( used to relate fact tables with different granularity)
  • 22. 
    ------------------ dimensions at least two fact tables share two dimensions.Power behind the data warehouse- Adding facts-Adding dimensions-Adding measures -Adding dimension attributes
  • 23. 
    ---------------- is filtering data in a cube?
  • 24. 
    ------------ looking at data in a different way, not data change just change, way rows in a column looks.
  • 25. 
    -----------------  to finer level of detail.
  • 26. 
    ------------------ look up aggregate data opposite of drill down hierarchy.
  • 27. 
    These are examples of -----------------------?Year -------> Month ----------> DateYear -------->Week -----------> Date-Date breaks up Month and Month breaks up Year
  • 28. 
    Normalization is important --------------- the data warehouse because of data integrity, space, duplication.
  • 29. 
     a data warehouse uses more --------------- because its denormalized.
  • 30. 
    A ---------------- database has more tables cause its normalized.
  • 31. 
    ----------------- is easier in data warehouse not normalized easy for user.
  • 32. 
    ----------------- of a fact table is combination of a fact tables lowest level.Ex. store, month, state.
  • 33. 
    These characteristics describe -------------------- types ?Sum, Avg, count count distinct.
  • 34. 
    Use ------------ aggregation type to add up fields?
  • 35. 
    Use --------------- aggregation type to to count all order keys for example tell me how many distinct order keys there are and count them, thats how many orders i have.
  • 36. 
    -----------------  data stored in the cube.
  • 37. 
    ------------------ data stored on the relational side.
  • 38. 
    --------------- attribute break up another table.
  • 39. 
    ------------------ attributes break up into atomic columns?
  • 40. 
    --------------- exception to the rule that allows snow flaking.
  • 41. 
    -------------- playing dimension, is a dimension connected or related multiple times to a fact table.
  • 42. 
    ---------------- fact table does not have measures only surrogate keys.
  • 43. 
    ---------------- is a dimension that does not have a dimension table that is stored in a fact table.
  • 44. 
    ------------------ key is the primary key of a dimension table stored directly in fact table.
  • 45. 
    It is impossible to implement a type 2 dimension without a --------- key?
  • 46. 
    ------------- fact table, orders, work requests, call records, etc.one row for each action.
  • 47. 
    --------------- snapshot, ex. inventory, count as a time period, one row for each item per time period.
  • 48. 
    ----------------- snapshot, lifecycle, one row for each lifecycle tracked.
  • 49. 
    -----------------  dimensions used to hold flags and other non related values, combine all these small values into a single dimension.
  • 50. 
    Information delivery have many types of ----------- like tourist, operators.
  • 51. 
    These are ------------------- applications?1. Fraud (block credit card)2. Risk management (know whos not paying bill)3.Customer segmentation ( who to market to and why)4. Market basket analysis( other people like this) like amazon.
  • 52. 
    Load times for fact tables.Transaction- load time 12000 recordsPeriodic snapshot 1000 qty loaded dependent on when months X 12 or X 52 for weeks 12000 records or 52000 recordsAccumulating snapshot - have 1000 records at the end of the year never got updated. This is the only fact table that has to get updated.