WorksheetsDatabase System, Data Models, and Relationship
Total questions: 91
Worksheet time: 46mins
Data are considered as:
Processed facts
Organized knowledge
Raw facts
Contextual information
Information is:
Raw data without context
Data processed into meaningful form
Metadata about data
Random facts
Which of the following is the foundation of information?
Reports
Metadata
Data
Knowledge
Good decisions in organizations require:
Raw facts
Knowledge without data
Accurate and timely information
Manual record keeping
A database is best defined as:
A random collection of data
A spreadsheet of facts
A shared, integrated computer structure storing data
A program for calculation
Metadata refers to:
Data about data
Historical data only
Temporary data
Incomplete data
A DBMS is:
Software for analyzing statistics
A collection of programs that manage database structure and access
A type of operating system
A spreadsheet application
Which of the following is not an advantage of DBMS?
Improved data sharing
Improved data security
More data redundancy
Improved productivity
A single-user database is also called:
Desktop database
Enterprise database
Workgroup database
Distributed database
An enterprise database supports:
Only one user
A small number of users
A large number of users across the organization
Only personal storage
A centralized database stores data:
On multiple sites
At a single site
In multiple user machines
On cloud only
A distributed database stores data:
Only in a central server
In several different sites
On a personal computer
In spreadsheets
A transactional database is also called:
Analytical database
Production or operational database
Metadata database
None of the above
A data warehouse is primarily used for:
Daily transactions
Tactical or strategic decision-making
Storing random logs
End-user security
File systems are predecessors of:
Operating systems
Databases
Data warehouses
Internet servers
A major flaw of file system data management was:
Too much structured independence
Data redundancy and inconsistency
Ease of use
Fast processing
Which of the following best describes file system storage?
A collection of file folders, each tagged and kept in a cabinet
A structured table of tuples
A hierarchical database
A relational model
Manual filing systems became inefficient because:
They were too flexible
They were cumbersome for large data collections
They had no metadata
They required DBMS software
A DBMS acts as:
A replacement for computers
The intermediary between the user and database
A data warehouse
A backup tool only
Which of these is a main function of DBMS?
Manage structure of the database and control data access
Only provide hardware storage
Generate automatic knowledge
Work as an operating system
Improved decision making is possible with DBMS because:
It creates raw facts automatically
It produces accurate and timely information
It removes the need for context
It guarantees knowledge without data
DBMS improves productivity because:
It increases redundancy
It eliminates all metadata
It provides integrated access to shared data
It reduces user interaction
Which of the following is not a role of DBMS?
Improved data sharing
Better data integration
Minimized inconsistency
Increased manual processing
Database design focuses on:
Identifying user passwords
Designing structure for end-user data
Improving hardware speed
Operating system upgrades
A well-designed database:
Causes errors in processing
Facilitates data management and accurate information generation
Requires redundant data
Makes tracing errors harder
A poorly designed database:
Is always more secure
Produces difficult-to-trace errors
Simplifies decision making
Eliminates metadata
Unstructured data refers to:
Data organized in tabular form
Data in original, raw state
Data formatted in XML
Only numerical values
Semistructured data:
Has no organization
Is partly processed (e.g., XML)
Is stored only in spreadsheets
Exists only in paper form
XML databases are useful because:
They support semistructured data
They eliminate redundancy
They store images only
They only support numeric values
The importance of database design lies in:
Identifying expected use of the database
Ignoring end-user needs
Creating redundancy
Reducing security
A data model is best described as:
A detailed physical storage plan
An abstraction of real-world objects or events
A hardware component of databases
A file management system
Data models are often:
Graphical
Audio-based
Hardware designs
Spreadsheets
Data modeling is:
A one-time activity
Iterative and progressive
Irrelevant for databases
Only required for small systems
The purpose of a data model is to:
Create redundancy
Organize data for various users
Replace operating systems
Simplify hardware architecture
Data models facilitate interaction among:
Designer, programmer, and end user
Database and operating system
Hardware and software
Managers only
A data model is an abstraction, meaning:
It hides all data
It simplifies complex real-world environments
It processes only numeric values
It replaces metadata
End users need different:
Types of software
Views and needs for data
Operating systems
Metadata copies
Which of the following is NOT a benefit of data models?
Standardized communication
Organization of data
Elimination of context
Improved understanding of processes
An entity in a data model is:
A file folder in a cabinet
Anything about which data are to be collected and stored
A database administrator
Only a numeric record
An attribute represents:
A restriction on data
A characteristic of an entity
A relationship
A storage unit
A relationship in data models describes:
A mathematical formula
An association among entities
The format of metadata
Hardware connections
A 1:M relationship means:
One instance of A relates to many instances of B
Many instances of A relate to one instance of B
Both A and B depending on direction
None of the above
A M:N relationship means:
One instance is related to only one other
Many instances of A relate to many instances of B
No entities are related
Only hierarchical data is supported
A 1:1 relationship means:
Each entity instance is related to only one other instance
One entity is related to many others
Many entities relate to many others
No constraints are applied
Constraints in a data model are:
Restrictions placed on the data
Extra attributes
Metadata definitions
Reports gene
Constraints in a data model are:
Restrictions placed on the data
Extra attributes
Metadata definitions
Reports generated from queries
Business rules are:
Descriptions of policies, procedures, or principles in an organization
Computer algorithms only
Temporary data structures
File system folders
Which of the following is NOT a source of business rules?
Company managers
Department managers
Random guesses
Operations manuals
Business rules must be:
Written, updated, and easy to understand
Hidden from users
Stored only as metadata
Ignored in database design
Business rules help:
Eliminate constraints
Standardize the company’s view of data
Reduce metadata
Replace DBMS software
Business rules typically translate into:
Nouns → entities, verbs → relationships
Numbers → constraints, text → metadata
Entities → attributes, verbs → XML
Files → tables, folders → relationships
The hierarchical model was developed in the:
1980s
1960s
1990s
2000s
The hierarchical model represents data as:
Upside-down tree structure
A flat file
A spreadsheet
A relational table
A segment in the hierarchical model is analogous to:
A record type
A constraint
An attribute
Metadata
A disadvantage of the hierarchical model is:
Simple to implement
Complex to manage
Supports standardization
Strong structural independence
The network model was created to:
Eliminate databases
Represent complex data relationships more effectively
Replace file folders
Only support XML
The organization responsible for creating the network model’s DBTG standard was:
IEEE
CODASYL
ISO
Oracle
In the network model, a schema refers to:
Conceptual organization of the entire database
Only the user’s portion
A single record only
A file system folder
A subschema is:
The portion of the database seen by application programs
The metadata for the entire system
A constraint
A hierarchical node
In the network model, an owner record type is equivalent to:
Attribute
Parent in hierarchical model
Child in hierarchical model
Constraint
A member record type in the network model is equivalent to:
Parent in hierarchical model
Child in hierarchical model
Attribute
Table
The relational database model provides a:
Physical view of data
Logical view of data
Hierarchical view of data
Network view of data
In the relational model, a relation is implemented as a:
Graph
Tree
Table
File folder
A row in a table is also called a:
Attribute
Tuple
Domain
Relation
A column in a table is also known as a(n):
Tuple
Attribute
Row
Key
A group of related entities is called:
Attribute set
Entity set
Record type
Constraint set
Each row in a table must be:
Redundant
Unique
Repeated
Null
A key is:
One or more attributes that determine other attributes
Always a single attribute
A relationship only
A constraint only
A composite key is:
A key with duplicate attributes
A key composed of more than one attribute
An attribute with no value
A secondary key
A superkey is:
Any key that uniquely identifies a row
Always the foreign key
A redundant constraint
A key with only one attribute
A candidate key is:
A foreign key in a relation
A superkey without unnecessary attributes
A duplicated attribute
A null value in the table
The primary key of a table:
Can contain nulls
Must uniquely identify each row
Is always composite
Is only used for indexing
Null values are NOT permitted in:
Foreign keys
Primary keys
Secondary keys
Candidate keys
Null values can represent:
Unknown or missing values
Always zero
Redundant attributes
Security constraints
A foreign key is:
A key used only in backup databases
An attribute that matches the primary key of another table
A redundant copy of a candidate key
Always a composite key
Referential integrity means:
Every foreign key refers to a valid row in another relation
Every primary key must have duplicates
All null values must be removed
All data must be encrypted
A secondary key is used:
To uniquely identify each row
Strictly for data retrieval purposes
To enforce referential integrity
As the only key allowed in a table
Controlled redundancy in relational databases:
Makes the database fail
Makes the relational database work by linking tables
Eliminates foreign keys
Ignores duplication completely
Redundancy exists only when:
There is unnecessary duplication of attribute values
A foreign key is used
Composite keys are applied
Tables share attributes
Null values can cause problems with functions such as:
SUM, AVERAGE, COUNT
SELECT and PROJECT
JOIN and UNION
CREATE and DROP
Relational operators include:
SELECT, PROJECT, JOIN
OPEN, SAVE, EXIT
ADD, REMOVE, UPDATE
COPY, CUT, PASTE
SELECT in relational algebra is used to:
Retrieve specific rows based on conditions
Retrieve specific columns
Combine tables
Delete tuples
PROJECT in relational algebra is used to:
Retrieve rows
Retrieve specific columns (attributes)
Combine two relations
Enforce integrity
JOIN in relational algebra is used to:
Merge rows with common attributes from two tables
Project only certain columns
Delete redundant tuples
Count the number of records
The system catalog is also known as:
Data warehouse
Data dictionary
Metadata store
All of the above
The data dictionary contains:
Metadata about database structures
Raw transaction data
Temporary files only
Only user passwords
Indexing in relational databases is important because it:
Slows down data access
Speeds up data retrieval
Prevents foreign keys
Eliminates metadata
Which of the following is NOT true about indexing?
It improves query performance
It eliminates the need for primary keys
It uses pointers to rows in tables
It supports faster searching
The relational model is easier to understand than:
File systems
Hierarchical and network models
Data warehouses
Spreadsheets
Relational tables provide:
Physical view of data
Logical view of related entities
Hierarchical metadata
Redundant file folders
The main advantage of relational databases is:
Flexibility, simplicity, and strong theoretical foundation
High redundancy
Dependence on physical storage
Lack of data integrity
