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Question
A data constraint is a limitation on data values.Answer
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Related questions
Q:
Suppose you have created a data model for an airline based on a variety of forms, reports, user interviews, data sources, etc. Your client comes forward with a new desired report that includes passenger weights and a relationship between passengers and employees, neither of which is in your E-R diagram. You should ________.
A) create a new, separate E-R diagram with just the new information
B) add the relationship and the weight attribute to the existing data model
C) add the relationship but not the weight attribute
D) add the weight attribute but not the relationship
Q:
Which of the following is not true about subtype entities?
A) Subtypes may be exclusive.
B) The supertype and subtypes will have the same identifier.
C) Subtypes are used to avoid a situation in which some attributes are required to be null.
D) All subtypes of a supertype must have the same attributes.
Q:
Recursive relationships only exist for one-to-one relationships.
Q:
Relationships among instances of a single entity class are called redundant relationships.
Q:
Subtypes can be exclusive or inclusive.
Q:
All weak entities must have a minimum cardinality of 1 on the entity on which it depends.
Q:
ID-dependent entities are a common type of weak entity.
Q:
An entity that represents something that can exist on its own is called a strong entity.
Q:
In an E-R model, the three types of minimum cardinality are mandatory, optional, and indeterminate.
Q:
In a 1:N relationship, the term parent refers to the N side of the relationship.
Q:
Relationships are classified by their cardinality.
Q:
In today's E-R models, attributes of relationships are still used.
Q:
Entities can be associated with one another in relationships.
Q:
In E-R modeling, entities within an entity class may have different attributes.
Q:
An entity instance is an occurrence of a particular entity.
Q:
Entities of a given type are grouped into entity classes.
Q:
What are general-purpose remarks columns, and why are they a problem?
Q:
Explain denormalization, and why it may be appropriate to denormalize a set of tables.
Q:
When accessing table structure, how do you determine how many rows are in a table?
Q:
To check for null values in a column in a table, use the SQL phrase ________.
A) IS
B) IS NOT
C) IS NULL
D) COUNT(NULL)
Q:
When a table is created using existing data from multiple sources, you are likely to find that the different sources code data in slightly different ways. This is an example of ________.
A) the multivalued, multicolumn problem
B) the inconsistent values problem
C) the missing values problem
D) the general-purpose remarks column problem
Q:
A classic example of unneeded normalization is when we are dealing with ________.
A) ZIP codes
B) sales orders and line items
C) association patterns
D) multivalued dependencies
Q:
The SQL keyword TOP ________.
A) limits the number of columns retrieved from a table
B) limits the number of rows retrieved from a table
C) limits the number of tables retrieved from a database
D) A and C
Q:
During the second step of assessing table structure, you are trying to determine ________.
A) relationships
B) candidate keys
C) updatability
D) efficiency
Q:
The first step in assessing table structure includes ________.
A) counting rows
B) examining columns
C) examining data values
D) Both A and B are correct
Q:
A null value in a column may indicate that there is an appropriate value for that attribute, but it is unknown.
Q:
An inconsistent values problem is created when different users have coded the same data entries differently.
Q:
Creating a read-only database is a job often given to beginning database professionals.
Q:
The SQL DELETE TABLE statement can be used to remove unneeded tables after the normalized tables are created and populated.
Q:
The SQL INSERT statement can be used to populate normalized tables.