Test your basic knowledge |

Data Warehousing

Subject : it-skills
Instructions:
  • Answer 50 questions in 15 minutes.
  • If you are not ready to take this test, you can study here.
  • Match each statement with the correct term.
  • Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.

This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.
1. A database table that maps (e.g.) manufacturing product IDs to sales product IDs






2. Fourth stage of the data warehouse data architecture design process






3. A summary record for a specific reporting period






4. A schema where dimensions may be connected to facts or to other dimensions






5. A group of conformed dimensions






6. A schema where dimensions are only connected to facts






7. Extract - Transform and Load






8. Look-up tables referred to by fact tables






9. Issues to consider when deciding EDW architecture: 1. Which database management system (DBMS) should be used? (ex: Crash Data" MS SQL) 2. Will ______________________ be used? 3. Will data migration tools be used to load the data warehouse?4. What too






10. Draws data from a data warehouse






11. Second stage of building a data mart






12. All database architectural fall into one of these two categories: EDW or _________ (independent data marts - data mart bus architecture - hub and spoke architecture - centralized data warehouse - federated data warehouse)






13. A visual display of the most important information needed by a particular user






14. Used to support day-to-day operations






15. These extend BI into automating and optimising processes






16. A daily snapshot for each day of a set period






17. An alternative term for a data warehouse






18. Fifth stage of building a data mart






19. Import data into a new database






20. A synonym for Business Intelligence






21. Fourth stage of building a data mart






22. First stage of the data warehouse data architecture design process






23. Data about the data






24. Second stage of the data warehouse data architecture design process






25. A logical design technique that presents data in a way that is optimised for high-performance access






26. A table containing foreign keys and values






27. Business applications - methods and tools that support the caputre and use of master data






28. Requesting more information about a particular fact






29. The level of detail required for a fact to be useful






30. A group of related fact tables






31. Online Analytical Processing






32. Dimensions which are shared by two or more facts






33. A tool that allows users to write queries without having to learn SQL






34. Take data from a source system






35. Third stage of building a data mart






36. A query created by a BI user






37. An operational system that provides data for a business information system






38. Make changes to data so that it is compatible with a new database






39. A single low-level fact representing a single business operation






40. A computer system used to support the day-to-day operations of an organisation






41. simplest and least costly architecture developed to operate independently of each other - poor solution






42. Requesting information about related facts






43. The business applications






44. Data accessible to the whole data warehouse






45. Most famous data warehouse where a maintable infastructure includes a centralized data warehouse that serves for the needs of all organizational units (most favored 39 percent)






46. Splitting data tables into smaller tables for efficient access.






47. First stage of building a data mart






48. Unlike independent data marts the marts are linked via middleware






49. Extract - Load and Transform






50. Allows multiple CPUs to process multiple queries simultaneously - providing scalability.