Standardization vsTransformation — Learn free

Self-check

Questions 1–10 of 10

1. In a Data Warehouse, what is normally kept in the Landing Zone?
Question 1A. A true copy of the source systems' dataB. The final dimensional model for reportingC. Only aggregated summary tablesD. The semantic layer for business usersE. Anonymised test data
2. What do the tables in the Landing and Staging zones preserve from the source systems?
Question 2A. The condition of the data but not the data modelB. The true data model but not the condition of the dataC. Neither the data model nor the condition of the dataD. Only the table namesE. Only the row counts
3. Which of these is an example of data standardisation?
Question 3A. Converting an OLTP data model into a dimensional modelB. Building a cube for a decision support systemC. Converting date formats such as DD/MM/YY and MM/DD/YY from different sources into one format like DD/MM/YYYYD. Creating a semantic layer for business usersE. Loading facts and dimensions into a star schema
4. What is data transformation about in the data world?
Question 4A. Fixing spelling mistakes in a columnB. Splitting a full name into first, middle and last nameC. Converting empty fields to NAD. Converting one data model to another, such as a source OLTP model into a Data Warehousing model, semantic layer or cubeE. Changing a string column to a numeric type
5. Which value is suggested for empty date rows during data standardisation?
Question 5A. 00/00/0000B. Today's dateC. The word NULLD. The date the row was loadedE. 01/01/9999
6. Why does data standardisation suggest converting a string field that only contains numbers into a numeric type?
Question 6A. Because running queries on digits or numbers is much fasterB. Because strings cannot be stored in a warehouseC. Because regulators require numeric columnsD. Because it reduces the need for backupsE. Because numbers are easier to encrypt
7. Between which zones does data transformation consume huge compute power?
Question 7A. From Source to LandingB. From Staging to Transformed ZoneC. From Landing to StagingD. From Transformed Zone back to LandingE. From Backup to Archive
8. By how much can compute power for data transformation be reduced if data standardisation is done first?
Question 8A. By 1% to 5%B. By 10% to 15%C. By 30% to 50%D. By 90% to 100%E. It cannot be reduced at all
9. Which of the following is NOT a data standardisation scenario?
Question 9A. Converting special characters to NAB. Fixing upper and lower cases so Aus, Aust and australia all become AustraliaC. Splitting a full name into First, Middle and Last NameD. Converting a source OLTP model into a dimensional model for servingE. Making a code field CHAR when it contains only characters
10. Why does serving always supersede processing as the reason for data transformation?
Question 10A. Because processing is free of costB. Because regulators mandate serving firstC. Because processing cannot be measuredD. Because standardisation already handles all processingE. Because serving is for end users, and the faster the results, the happier the customer