Ans: When using find, the format of the field value must be exact, for example 90.0 instead of 90. If the format is not exact, the value might not be found. However, Find is easier to use than a filter when the filter involves many columns.
Ans: Values of a field containing Text values can be any string of characters. For example "34544-1002" might be entered as a zip code. This value would not be possible for a Number datatype.
Ans: One cannot do arithmetic on Text values. Also, numbers will not sort in numerical order if they are Text values.
Ans: Use this Advanced Filter/Sort:
Field: | January |
Sort: | |
Criteria: | >=88 |
or: | <=0 |
Ans: False, you put values of a Text field in double quotes. You put a column name in square brackets like this [Age] or [PersonData.Age].
Data Anomaly A table containing a column that depends entirely on different column that is not the primary key. Each column in a table must depend only on the primary key and no other columns
Data Normalization Splitting a table into two or more tables to eliminate either repeated groups or data anomalies.
Foreign Key A column in one table that contains a primary key from another table.
Relationship Specifies one-many or many-many relationships between two tables by specifying the foreign keys and primary keys involved.
Ans: (a) Keep the data that is not part of a repeated group in the original table. (b) Move the repeated group to a new table. (c) Choose primary keys in the original and new tables. Add new id variables, if necessary, for primary keys. (d) Add the primary key of the original table to the new table as a foreign key.
Ans:
Field: | First Name | Last Name | Relationship | DependentName |
Table: | Athletes | Athletes | Dependents | Dependents |
Sort: | ||||
Show: | Yes | Yes | No | No |
Criteria: | "Wife" | "Tricia" | ||
or: |
Ans:
Field: | First Name | Last Name | PhoneType | PhoneNumber |
Table: | Athletes | Athletes | AthletePhoneNumbers | AthletePhoneNumbers |
Sort: | ||||
Show: | No | No | Yes | Yes |
Criteria: | "Stan" | "Ironman" | ||
or: |
Ans:
Field: | First Name | Last Name | Relationship | DependentName |
Table: | Athletes | Athletes | Dependents | Dependents |
Sort: | ||||
Show: | No | No | No | Yes |
Criteria: | "Leroy" | "Slaughter" | "Child" | |
or: |
Ans:
Field: | First Name | Last Name | Salary |
Table: | Athletes | Athletes | Athletes |
Sort: | |||
Show: | Yes | Yes | No |
Criteria: | >10000000 | ||
or: |
Ans:
Field: | First Name | Last Name | PhoneNumber |
Table: | Athletes | Athletes | DependentPhoneNumbers |
Sort: | |||
Show: | No | No | Yes |
Criteria: | "Aaron" | "Fleetfoot" | |
or: |