Database Midterm

Information is processed data.

True

In practice, databases today may contain either data or information.

True

Metadata are data that describe the properties of other data.

True

Databases were developed as the first application of computers to data processing.

False

File processing systems have been replaced by database systems in most critical business applications today.

True

Unplanned duplicate data files are the rule rather than the exception in file processing systems.

True

The systems development life cycle is the traditional methodology used to develop, maintain, and replace information systems.

True

The steps of the systems development life cycle can only be viewed as a linear process.

False

The repository is populated during the analysis phase of the systems development life cycle.

True

The physical structure and storage organization of the database are decided upon during the implementation phase of the systems development life cycle.

False

Database processing programs are coded and tested during the design stage of the systems development life cycle.

False

Data from prior systems is converted to the new system during the implementation phase of the systems development life cycle.

True

Database maintenance is typically the longest step of the database development process.

True

With the traditional file processing approach, each application shares data files, thus enabling much data sharing.

False

A well-structured database establishes the entities between relationships in order to derive the desired information.

False

A relational database establishes the relationships between entities by means of a common field.

True

Data redundancy is used to establish relationships between data but is never used to improve database performance.

False

Redundancy increases the risk of inconsistent data.

True

A user view is how the user sees the data when it is produced.

False

A constraint is a rule in a database system that can be violated by users.

False

Reduced program maintenance is an advantage of file processing systems.

False

Cost and complexity are just two of the disadvantages of database processing.

True

The term legacy system refers to a newly installed database management system.

Fasle

An enterprise data model describes the scope of data for only one information system.

False

Enterprise modeling sets the range and general contents of organizational databases.

True

Prototyping is a type of rapid application development.

True

In prototyping, implementation and maintenance activities are repeated as necessary until the product is correct.

True

In 1998, ANSI/SPARC published an important document describing the three-schema architecture.

True

A physical schema contains the specifications for how data from a conceptual schema are stored in a computer's secondary memory.

True

Personal databases are designed to support a small group of individuals working together on a project.

False

The most common way to support a group of individuals who work together on a project or group of similar projects is with a two-tier client/server database.

True

Applications built with a multitier architecture are meant to support departments.

True

An extranet uses Internet protocols to establish limited access to company data by the company's customers and suppliers.

True

Most systems developers believe that data modeling is the least important part of the systems development process.

False

The E-R model is used to construct a conceptual model.

True

In an E-R diagram, strong entities are represented by double-walled rectangles.

False

In an E-R diagram, an associative entity is represented by a rounded rectangle.

True

Data modeling is about documenting rules and policies of an organization that govern data.

True

The purpose of data modeling is to document business rules about processes.

False

A business rule is a statement that defines or constrains some aspect of the business.

True

The intent of a business rule is to break down business structure.

False

Data names should always relate to business characteristics.

True

A good data definition is always accompanied by diagrams, such as the entity-relationship diagram.

True

An entity is a person, place, object, event, or concept in the user environment about which the organization wishes to maintain data.

True

A single occurrence of an entity is called an entity instance.

True

The relationship between a weak entity type and its owner is an identifying relationship.

True

An attribute whose values can be calculated from related attribute values is called a derived attribute.

True

A multivalued attribute may take on more than one value for a particular entity instance.

True

When choosing an identifier, choose one that will not change its value often.

True

One reason to use an associative entity is if the associative entity has one or more attributes in addition to the identifier.

True

The degree of a relationship is the number of attributes that are associated with it.

False

The relationship among the instances of three entity types is called a unary relationship.

False

A cardinality constraint tells what kinds of properties are associated with an entity.

False

Participation in a relationship may be optional or mandatory.

True

A ternary relationship is equivalent to three binary relationships.

False

A time stamp is a time value that is associated with a data value.

True

Relationships represent action being taken using a verb phrase.

True

A subtype is a generic entity that has a relationship with one or more entities at a lower level.

False

One of the major challenges in data modeling is to recognize and clearly represent entities that are almost the same.

True

An entity instance of a subtype represents the same entity instance of the supertype.

True

A member of a subtype does NOT necessarily have to be a member of the supertype.

False

Supertype/subtype relationships should be used when the instances of a subtype participate in no relationships which are unique to that subtype.

False

Specialization is the reverse of generalization.

True

Generalization is a top-down process.

False

A completeness constraint may specify that each entity of the supertype must be a member of some subtype in the relationship.

True

The total specialization rule states that an entity instance of a supertype is allowed not to belong to any subtype.

False

When the total specialization rule is set for a supertype/subtype relationship, one could roughly compare the supertype to an abstract class in object-oriented programming.

False

The disjoint rule specifies that if an entity instance of the supertype is a member of one subtype, it MUST simultaneously be a member of another subtype.

False

The overlap rule specifies that if an entity instance of the supertype is a member of one subtype, it can simultaneously be a member of two (or more) subtypes.

True

When subtypes are overlapping, an additional field must be added to the supertype to act as a discriminator.

False

A subtype can become a supertype if the subtype has other subtypes beneath it.

True

In a supertype/subtype hierarchy, attributes are assigned at the highest logical level that is possible in the hierarchy.

True

Subtypes at the lowest level of a hierarchy do not inherit attributes from their ancestors.

False

A universal data model is a generic or template data model that can be reused as a starting point for a data modeling project.

True

Creating a data model from a packaged data model requires much more skill than creating one from scratch.

False

Because a purchased data model is extensive, you begin by identifying the parts of the data model that apply to your data modeling situation.

True

You will never need to map data in current databases to data in a packaged data model.

False

Mapping existing data to new data in a packaged data model is useful for developing migration plans.

True

It is easy to miss the opportunity to visualize future requirements shown in the full data model when using a packaged data model.

True

Data structures include data organized in the form of tables with rows and columns.

True

Data integrity consists of powerful operations to manipulate data stored in relations.

False

A composite key consists of only one attribute.

False

A primary key is an attribute that uniquely identifies each row in a relation.

True

A foreign key is a primary key of a relation that also is a primary key in another relation.

False

One property of a relation is that each attribute within a relation has a unique name.

True

There can be multivalued attributes in a relation.

False

Unlike columns, the rows of a relation may not be interchanged and must be stored in one sequence.

False

The allowable range of values for a given attribute is part of the domain constraint.

True

All values that appear in a column of a relation must be taken from the same domain.

True

The entity integrity rule states that a primary key attribute can be null.

False

In the relational data model, associations between tables are defined through the use of primary keys.

Fasle

A referential integrity constraint is a rule that maintains consistency among the rows of two relations.

True

A cascading delete removes all records in other tables associated with the record to be deleted.

True

The truncate table statement in SQL creates a new table.

False

A well-structured relation contains minimal redundancy and allows users to manipulate the relation without errors or inconsistencies.

True

CASE tools can model more complex data relationships, such as ternary relationships.

False

When a regular entity type contains a multivalued attribute, two relations are created.

True

When transforming a weak entity, one should create one relation with both the attributes of the strong entity and the attributes of the weak entity.

False

The primary key of the many side migrates to the one side when transforming a one-to-many relationship.

False

When transforming a one-to-one relationship, a new relation is always created.

Fasle

When transforming a unary many-to-many relationship to relations, a recursive foreign key is used.

False

The relational data model does, at this time, directly support subtype/supertype relationships.

False

When normalizing, the goal is to decompose relations with anomalies to produce smaller, well-structured relations.

True

A candidate key is an attribute, or combination of attributes, that uniquely identifies a row in a relation.

True

A relation is in first normal form if it has no more than one multivalued attribute.

False

A partial functional dependency is a functional dependency in which one or more nonkey attributes are functionally dependent on part (but not all) of the primary key.

True

A transversal dependency is a functional dependency between two or more nonkey attributes.

False

View integration is the process of merging relations together.

False