CS 682 Chapter 8
False Rationale: A recursive relationship is a relationship with a degree of one (1), only one entity participates in the relationship.
36. A recursive relationship is a relationship with a degree of infinity, because there is no limit to how many entities participate in the relationship.
True
37. A recursive relationship identifies a relationship that may exist between different instances of the same entity.
True
38. A ternary relationship is a relationship among three entities.
True
39. The relationship between a student entity and a curriculum entity would be classified as recursive.
True
4. An entity is something about which the business needs to store data.
True
40. In a one-to-many relationship, the parent is the entity on the "one" side.
True
41. A foreign key in a child entity always matches the primary key in the parent entity.
False
42. A foreign key in the parent entity always matches the primary key in the child entity.
True
43. Nonidentifying relationships are those in which each of the participating entities has its own independent primary key. That is, none of the primary key attributes is shared.
False
44. Nonidentifying relationships are those in which each of the participating entities has dependent primary keys.
True
45. Identifying relationships are those in which the parent entity contributes its primary key to become part of the primary key of the child entity.
True
46. A nonspecific relationship is a many-to-many relationship
True
47. A non-specific relationship is one in which many instances of one entity are associated with many instances of another entity.
False Rationale: A many-to-many relationship is one in which many instances of one entity are associated with many instances of another entity.
48. A many-to-many relationship is one in which many entities are associated with other attributes of a different entity.
True
49. Generalization is a technique wherein the attributes that are common to several types of an entity are grouped into their own entity, called a supertype.
True
5. An entity is a class of persons, places, objects, events or concepts about which we need to capture and store data.
False Rationale: Generalization is a technique wherein the attributes that are common to several types of an entity are grouped into their own entity, called a supertype.
50. Generalization is a technique wherein the domains common to several types of attributes are grouped into their own entity, called an associate entity.
True
51. An entity subtype is an entity whose instances inherit some common attributes from an entity supertype and then add other attributes that are unique to an instance of the subtype.
False Rationale: An entity subtype is an entity whose instances inherit some common attributes from an entity supertype and then add other attributes that are unique to an instance of the subtype.
52. An entity supertype is an entity whose instances inherit some common attributes from an entity subtype and then add other attributes that are unique to an instance of the supertype.
True
53. An enterprise data model typically identifies only the most fundamental of entities of the enterprise.
False Rationale: An enterprise data model typically identifies only the most fundamental of entities.
54. An enterprise data model typically identifies and defines only the most complex entities used by the enterprise.
True
55. The data model for a single information system is usually called an application data model.
True
56. The context data model is prepared during the problem analysis phase and only includes entities and relationships, but no attributes.
True
57. The requirements analysis results in a logical data model that is developed in stages as follows: (1) context data model; (2) key-based data model; (3) fully attributed data model; and (4) the normalized data model.
False Rationale: The requirements analysis results in a logical data model that is developed in stages as follows: (1) context data model; (2) key-based data model; (3) fully attributed data model; and (4) the normalized data model.
58. The requirements analysis results in a physical data model that is developed in stages as follows: (1) normalized data model; (2) key-based data model; (3) fully attributed data model; and (4) the context data model.
True
59. During systems design, the logical data model will be transformed into a physical data model.
False Rationale: An entity is a class of persons, places, objects, events or concepts about which we need to capture and store data.
6. An identity is a class of persons, places, objects, events, or concepts about which we need to capture and store data.
Answer: False Rationale: During systems design, the logical data model will be transformed into a physical data model.
60. During the requirements phase, the physical data model is transformed into the logical data model.
Answer: False Rationale: Another name for the physical data model is the database schema.
61. Another name for the logical data model is the database schema.
True
62. The data model is metadata - that is, it is data about data.
True
63. The value of a key should not change over the lifetime of each entity instance.
False Rationale: The value of a key should not change over the lifetime of each entity instance.
64. The value of a key can change over the lifetime of each entity instance.
False Rationale: The value of a key cannot be null
65. The value of a key can be null.
True
66. Controls must be installed to ensure that the value of a key is valid.
True
67. An intelligent key is a business code whose structure communicates data about an entity instance (such as its classification, size or other properties).
True
68. The authors of your textbook recommend the use of intelligent keys since they can be quickly processed by humans without the assistance of a computer.
True
69. Some experts suggest that you avoid the use of intelligent keys when designing your data model. They argue that because characteristics can change it violates the rule that the value of a key should not change over the lifetime of each entity instance.
True
7. An entity instance is a single occurrence of an entity.
True
70. Serial codes assign sequentially generated numbers to entity instances.
True
71. Alphabetic codes use finite combinations of letters (and possibly numbers) to describe entity instances.
True
72. In significant position codes, each digit or group of digits describes a measurable or identifiable characteristic of the entity instance.
True
73. Significant position codes are frequently used to code inventory items.
True
74. Hierarchical codes provide a top-down interpretation for an entity instance by factoring an item into its group, subgroup and so forth.
False Rationale: Scan each use-case narrative for nouns.
75. If use-case narratives have been written during the requirements analysis phase, analysts can scan them for verbs to discover data attributes and entities.
Answer: False Rationale: It is not a trivial task to identify the remaining data attributes. To accomplish this task, it is necessary to have a thorough understanding of the data attributes for the system.
76. Once the data model has been defined, it is trivial to identify the remaining data attributes.
True
77. Many organizations have naming standards and approved abbreviations for data attributes.
True
78. A good data model is simple.
True
79. A good data model is essentially nonredundant.
False Rationale: An entity instance is a single occurrence of an entity.
8. An entity existence is a single occurrence of an entity.
False Rationale: Each attribute, other than foreign keys, describes at most one entity.
80. In a good data mode, each data attribute describes at most one entity.
True
81. A good data model should be flexible and adaptable to future needs.
False Rationale: A good data model should be flexible and adaptable to future needs.
82. A good data model is inflexible because it is an accurate representation of the business data requirements.
False Rationale: Data analysis is a process that prepares a data model for implementation as a simple, nonredundant, flexible and adaptable database through a technique called normalization.
83. Data analysis is a process that prepares a logical model for implementation as a redundant, explicit, and finite database through a technique called generalization.
True
84. An entity is in first normal form (1NF) if there are no attributes that can have more than one value for a single instance of the entity.
True
85. An entity is in second normal form (2NF) if it is already in 1NF and if the values of all nonprimary key attributes are dependent on the full primary key - not just part of it.
True
86. An entity is in third normal form (3NF) if it is already in 2NF and if the values of its non-primary key attributes are not dependent on any other non-primary key attributes.
False Rationale: An entity is in second normal form (2NF) if it is already in 1NF and if the values of all nonprimary key attributes are dependent on the full primary key not just part of it.
87. An entity is in third normal form (3NF) if it is already in 2NF and if the values of all nonprimary key attributes are dependent on the full primary key - not just part of it.
False Rationale: An entity is in first normal form (1NF) if there are no attributes that can have more than one value for a single instance of the entity.
88. An entity is in first normal form (1NF) if the values of its nonprimary key attributes are not dependent on any other nonprimary key attributes.
False Rationale: Transitive dependency is an error that is removed by 3NF.
89. One form of 3NF makes sure that transitive dependencies exist in each entity.
True
9. An attribute is a descriptive property or characteristic of an entity.
True
90. A data-to-location CRUD matrix is a table in which the rows indicate entities (and possible attributes); the columns indicate locations; and the cells (the intersection of the rows and columns) document level of access where C=create; R=read; U=update; and D=delete or deactivate.
False Rationale: A data-to-location CRUD matrix is a table in which the rows indicate entities (and possible attributes); the columns indicate locations; and the cells (the intersection of the rows and columns) document level of access where C=create; R=read; U=update; and D=delete or deactivate.
91. A data-to-location CRUD matrix is a table in which garbage values that fall outside the domain of an attribute are identified and used for data verification.
True
92. Many nonspecific relationships can be resolved into two one-to-many relationships using an associative entity.
False Rationale: A generalization hierarchy can be any number of levels deep. See Figure 8-11
93. A generalization hierarchy can be at most two levels deep.
all of these
94. Which of the following is a category of entities? A) person B) place C) object D) concept E) all of these
an attribute
95. A descriptive property or characteristic of an entity is: A) a domain B) an attribute C) an entity instance D) an entity existence E) none of these
True
35. A recursive relationship is when only one entity participates in the relationship.
True
1. Data modeling is a technique for defining business requirements for a database.
True
10. A compound attribute is one that actually consists of other attributes that are logically grouped together.
the alternate key
100. Any candidate key that is not selected to become the primary key is called: A) the entity key B) the concatenated key C) the subsetting key D) the domain key E) the alternate key
False Rationale: A compound attribute is one that actually consists of other attributes that are logically grouped together.
11. A compound attribute is an attribute that will be expanded into a separate entity.
True
12. The data type of an attribute defines what type of data can be stored in that attribute.
True
13. Example data types include: numbers, text, memo, date, time, yes/no, Boolean, value set, or image.
True
14. The domain of an attribute defines what values an attribute can legitimately take on.
False Rationale: The default value for an attribute is the value that will be recorded if not specified by the user.
15. The domain value for an attribute is the value that will be recorded if not specified by the user.
True
16. A key is an attribute, or group of attributes, that assumes a unique value for each entity instance. It is sometimes called an identifier.
False Rationale: A key is an attribute, or group of attributes, that assumes a unique value for each entity instance. It is sometimes called an identifier.
17. A key is an attribute or group of attributes that assumes a unique value for each entity instance. It is sometimes called the domain of the attribute.
True
18. A concatenated key is a group of attributes that uniquely identifies an instance of an entity.
True
19. A concatenated key is also known as a composite key or a compound key.
False Rationale: Data modeling is a technique for organizing and documenting a system's data.
2. Data modeling is a technique for organizing and documenting a system's logical and physical models.
False Rationale: A candidate key may be a single attribute or a concatenated key.
20. A candidate key must be a single attribute.
True
21. A candidate key may be a single attribute or a concatenated key.
True
22. A primary key is that candidate key that will most commonly be used to uniquely identify a single entity instance.
True
23. An example of domain would be an attribute called grade where the values could only be A, B, C, D, E, or F.
True
24. An alternate key is also known as a secondary key.
True
25. A subsetting criteria is an attribute or concatenated attribute whose finite values divide all entity instances into useful subsets.
True
26. A subsetting criteria is also known as an inversion entry
False Rationale: A subsetting criteria is an attribute or concatenated attribute whose finite values divide all entity instances into useful subsets.
27. A subsetting criteria is a domain of attributes whose values are limitless to allow for a variety of subsets to be constructed from a database.
True
28. A relationship is a natural business association that exists between one or more entities.
False Rationale: A relationship may represent an event that links the entities or merely a logical affinity that exists between the entities.
29. A relationship may represent an event that links the entities or merely a physical affinity that exists between the entities.
True
3. Data modeling is sometimes called database modeling because a data model is eventually implemented as a database.
False Rationale: Relationships are bi-directional.
30. All data model relationships are unidirectional.
True
31. Because all relationships are bi-directional in an entity relationship diagram, cardinality must be defined in both directions for every relationship.
False
32. Conceptually cardinality defines the minimum and maximum attributes that can be added to an entity.
True
33. The degree of a relationship is the number of entities that participate in the relationship.
False
34. The domain of a relationship is the number of entities that participate in the relationship.
D both A & B defines what type of data can be stored in an attribute. could be text, number, date, time, yes/no, value set or image.
96. A data type: A) defines what type of data can be stored in an attribute. B) could be text, number, date, time, yes/no, value set or image. C) consists of compound attributes. D) both (A) and (B). E) none of these
domain
97. What defines what values an attribute can legitimately take on? A) realm B) entity C) relationship D) domain E) none of these
default value
98. The value that is recorded in an attribute if a user does not specify one is known as the: A) domain B) key C) default value D) data type E) none of these
key
99. An attribute or group of attributes that assumes a unique value for each entity instance is a: A) domain B) key C) default value D) data type E) none of these
