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T/F

1. Data and information are essentially the same thing.
F
2. Data processing can be as simple as organizing data to reveal patterns.
T
3. Data is the result of processing raw facts to reveal its meaning.
F
4. When data are entered into a form and saved, they are placed in the underlying database as knowledge.
F
5. Data constitute the building blocks of information.
T
6. Metadata describe the data characteristics and the set of relationships that links the data found within the database
T
7. The only way to access the data in a database is through the DBMS.
T
8. Database programming languages receive all application requests and translate them into the complex operations required to fulfill those requests.
F
9. The DBMS reveals much of the database’s internal complexity to the application programs and users.
F
10. One disadvantage of the DBMS is that it increases the risk of data security breaches.
F
11. An operational database is sometimes referred to as an enterprise database.
F
12. A data warehouse can store data derived from many sources.
T
13. The same data might be simultaneously structured and unstructured depending on the intended processing.
T
14. Corporations use only structured data.
F
15. Field refers to a collection of related records.
F
16. Data anomaly is defined as the condition in which all of the data in the database are consistent with the real-world events and conditions.
F
17. Structural dependence exists when it is possible to make changes in the file structure without affecting the application program’s ability to access the data.
F
18. An advantage of database systems is that you needn’t perform frequent updates and apply latest patches.
F
19. One disadvantage of a database system over previous data management approaches is increased costs.
T
1
1
2. An implementation-ready data model needn’t necessarily contain enforceable rules to guarantee the integrity of the data.
F
3. An implementation-ready data model should contain a description of the data structure that will store the end-user data.
T
4. Within the database environment, a data model represents data structures with the purpose of supporting a specific problem domain.
T
5. Even when a good database blueprint is available, an applications programmer’s view of the data should match that of the manager and the end user.
F
6. In the context of data models, an entity is a person, place, thing, or event about which data will be collected and stored.
T
7. Database designers determine the data and information that yield the required understanding of the entire business.
F
8. Business rules apply to businesses and government groups, but not to other types of organizations such as religious groups or research laboratories.
F
9. Business rules must be rendered in writing.
T
10. In an SQL-based relational database, each table is dependent on every other table.
F
11. In an SQL-based relational database, rows in different tables are related based on common values in common attributes
T
12. A disadvantage of the relational database management system (RDBMS) is its inability to hide the complexities of the relational model from the user.
F
13. Each row in the relational table is known as an entity instance or entity occurrence in the ER model.
T
14. In Chen notation, entities and relationships have to be oriented horizontally; not vertically.
F
15. M:N relationships are not appropriate in a relational model.
T
16. Today, most relational database products can be classified as object/relational.
T
18. The external model is the representation of the database as “seen” by the DBMS.
F
17. The network model has structural level dependence.
T
19. The hierarchical model is software-independent.
F
20. The relational model is hardware-dependent and software-independent.
F
1. The practical significance of taking the logical view of a database is that it serves as a reminder of the simple file concept of data storage.
T
2. You can think of a table as a persistent representation of a logical relation.
T
3. The order of the rows and columns is important to the DBMS.
F
4. Character data can contain any character or symbol intended for mathematical manipulation.
F
5. The row’s range of permissible values is known as its domain.
F
6. Each table in a relational database must have a primary key.
T
7. The idea of determination is unique to the database environment.
F
8. Only a single attribute, not multiple attributes, can define functional dependence.
F
9. If the attribute (B) is functionally dependent on a composite key (A) but not on any subset of that composite key, the attribute (B) is fully functionally dependent on (A).
T
10. A null is created when you press the Enter key or the Tab key to move to the next entry without making a prior entry of any kind.
T
11. Depending on the sophistication of the application development software, nulls can create problems when functions such as COUNT, AVERAGE, and SUM are used.
T
12. RDBMSs enforce integrity rules automatically.
T
13. Relational algebra defines the theoretical way of manipulating table contents using relational operators.
T
14. The SELECT operator yields a vertical subset of a table.
F
15. The DIFFERENCE operator subtracts one table from the other.
T
16. In a natural join, the column on which the join was made occurs twice in the new table.
F
17. The DIVIDE operation uses one singlecolumn table (e.g., column “a”) as the divisor and one twocolumn table (e.g., columns “a” and “b”) as the dividend.
T
18. A data dictionary is sometimes described as “the database designer’s database” because it records the design
decisions about tables and their structures.
T
19. The onetomany (1:M) relationship is easily implemented in the relational model by putting the foreign key of the “1” side in the table of the “many” side as a primary key.
F
20. As rare as 1:1 relationships should be, certain conditions absolutely require their use.
T
1. As rare as 1:1 relationships should be, certain conditions absolutely require their use.
F
2. The Crow’s Foot notation is less implementationoriented than the Chen notation.
F
3. An entity in the entity relationship model corresponds to a table in the relational environment.
T
4. In the entity relationship model, a table row corresponds to an entity instance.
T
5. In the Chen and Crow’s Foot notations, an entity is represented with a rectangle containing the entity’s name.
T
6. In the original Chen notation, each attribute is represented by an oval with the attribute name connected to an entity rectangle with a line.
T
7. Software vendors have adopted the Chen representation because of its compact representation.
F
8. A composite identifier is a primary key composed of more than one attribute.
T
9. The Crow’s Foot notation easily identifies multivalued attributes.
F
10. Composite attributes make it easier to facilitate detailed queries.
F
11. Relationships between entities always operate in one direction.
F
12. Connectivities and cardinalities are established by concise statements known as business rules.
T
13. In Chen notation, there is no way to represent cardinality.
F
14. In implementation terms, an entity is existence-dependent if it has a mandatory primary key.
F
15. A weak relationship exists if the primary key of the related entity contains at least one primary key component of the parent entity.
F
16. In a 1:M relationship, to avoid the possibility of referential integrity errors, the data of the “1” side must be loaded first.
T
17. Unary relationships are common in manufacturing industries.
T
18. Referential integrity and participation are both bidirectional, meaning that they must be addressed in both directions along a relationship.
F
19. A weak entity has a primary key that is partially or totally derived from the parent entity in the relationship.
T
20. The existence of a mandatory relationship indicates that the minimum cardinality is 0 or 1 for the mandatory entity.
F
21. To implement a small database, a database designer must know the “1” and the “M” sides of each relationship and whether the relationships are mandatory or optional.
T
22. The process of database design is a sequential process.
F
1. The process of database design is a sequential process.
T
2. Normalization produces a lower normal form.
F
3. Normalization is a process that is used for changing attributes to entities.
F
4. In order to meet performance requirements, portions of the database design may need to be occasionally denormalized.
T
5. Denormalization produces a lower normal form.
T
6. Normalization is a very important database design ingredient, and the highest level is always the most desirable.
7. Reporting anomalies in a table can cause a multitude of problems for managers and can be fixed through application programming.
F
8. Data redundancy produces data anomalies.
T
9. A table is in BCNF if every determinant in the table is a candidate key.
T
10. A table is in BCNF if every determinant in the table is a foreign key.
F
11. A dependency based on only a part of a composite primary key is known as a partial dependency.
T
12. A table is in fourth normal form if it is in third normal form and has no independent multivalued dependencies.
T
13. Relational models view data as part of a table or collection of tables in which all key values must be identified.
T
14. The objective of normalization is to ensure that each table conforms to the concept of well-formed relations.
T
15. Repeating groups must be eliminated by ensuring that each row defines a single entity.
T
16. Dependency diagrams are very helpful in getting a bird’seye view of all the relationships among a table’s attributes.
T
17. Dependencies that are based on only a part of a composite primary key are called transitive dependencies.
F
18. All relational tables satisfy the 1NF requirements.
T
19. In the context of partial dependencies, data redundancies occur because every row entry requires duplication of data.
T
20. Converting a database format from 1NF to 2NF is a complex process.
F
21. Since a partial dependency can exist only if a table’s primary key is composed of several attributes, if a table in 1NF has a single-attribute primary key, then the table is automatically in 2NF.
T
22. A table is in 2NF if it is in 1NF, and it includes no partial dependencies.
T
23. It is possible for a table in 2NF to exhibit transitive dependency, where the primary key may rely on one or more nonprime attributes to functionally determine other nonprime attributes.
T
24. A determinant is any attribute whose value determines other values within a column.
F
26. Data stored at their highest level of granularity are said to be atomic data.
F
27. Atomic attributes are attributes that can be further subdivided.
F
28. Normalization should be part of the design process.
T
29. Normalization represents a micro view of the entities within the ERD.
T
30. The combination of normalization and ER modeling yields a useful ERD, whose entities can be translated into appropriate relationship structures.
F
31. A good relational DBMS excels at managing denormalized relations.
F
32. The advantage of higher processing speed must be carefully weighed against the disadvantage of data anomalies.
T
33. Normalization purity is often easy to sustain in the modern database environment.
F
34. Unnormalized database tables often lead to various data redundancy disasters in production databases.
T
35. Attributes should clearly define participation, connectivity, and document cardinality.
F

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