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CS403: Introduction to Modern Database Systems Certification Exam Answers

Modern database systems have evolved significantly from their early counterparts, adapting to the demands of contemporary data management needs. Here’s a brief introduction to some key aspects of modern database systems:

  1. Relational Databases: Relational databases remain a cornerstone of modern data management. They organize data into tables with rows and columns, allowing for efficient querying and manipulation using Structured Query Language (SQL). Examples include MySQL, PostgreSQL, and Oracle Database.
  2. NoSQL Databases: NoSQL (Not Only SQL) databases emerged to address scalability, flexibility, and performance issues not adequately handled by traditional relational databases. They can handle unstructured or semi-structured data and are commonly used in web applications, big data, and real-time analytics. Types of NoSQL databases include document-oriented (e.g., MongoDB), key-value stores (e.g., Redis), column-oriented (e.g., Apache Cassandra), and graph databases (e.g., Neo4j).
  3. NewSQL Databases: NewSQL databases aim to combine the benefits of traditional relational databases with the scalability and performance characteristics of NoSQL databases. They provide strong consistency, ACID transactions, and SQL support while offering horizontal scalability and distributed architecture. Examples include Google Spanner and CockroachDB.
  4. Distributed Databases: With the proliferation of cloud computing and the need for high availability and fault tolerance, distributed databases have become crucial. They distribute data across multiple nodes in a network, enabling horizontal scalability and fault tolerance. Distributed databases include both traditional relational databases (e.g., Amazon RDS) and NoSQL/NewSQL solutions (e.g., Amazon DynamoDB, Google Bigtable).
  5. In-Memory Databases: In-memory databases store data primarily in system memory (RAM) rather than on disk, resulting in faster read and write operations. They are ideal for applications requiring high-speed data access, such as real-time analytics and caching. Examples include Redis, Memcached, and SAP HANA.
  6. Columnar Databases: Columnar databases store data in columns rather than rows, making them highly efficient for analytics workloads that involve querying large datasets. They excel in data warehousing and business intelligence applications where aggregate queries are common. Examples include Amazon Redshift, Apache Parquet, and ClickHouse.
  7. Time-Series Databases: Time-series databases are optimized for handling data organized by time stamps, such as IoT sensor data, financial market data, and log files. They offer efficient storage and retrieval of time-stamped data and support specialized query operations for time-based analysis. Examples include InfluxDB, Prometheus, and TimescaleDB.
  8. Graph Databases: Graph databases are designed for managing and querying highly connected data, such as social networks, recommendation systems, and network analysis. They represent data as graphs composed of nodes, edges, and properties, enabling efficient traversal of relationships. Examples include Neo4j, Amazon Neptune, and ArangoDB.

Modern database systems continue to evolve to meet the diverse needs of today’s applications, leveraging innovations in hardware, software, and distributed computing technologies.

CS403: Introduction to Modern Database Systems Exam Quiz Answers

  • 32
  • It is 32.
  • It is 32 degrees.
  • It is 32 degrees Fahrenheit.
  • 32
  • I am 32
  • It is 32 degrees.
  • It is 32 outside.
  • A system for managing data
  • A set of data for a particular application
  • A major subsystem of an operating system
  • A reusable, organized collection of related data
  • Backing up hard drives or other storage disks
  • Recording the behavior of real time embedded systems
  • Storing and retrieving enterprise data for business systems
  • Performing computations for scientific and engineering application systems
  • Network
  • Relational
  • Hierarchical
  • Object-oriented
  • Network
  • Relational
  • Hierarchical
  • Object-oriented
  • Logical data independence
  • Physical data dependence
  • Physical data independence
  • Physical schema refinement
  • User view
  • Table contents
  • Logical schema
  • Physical schema
  • a meta table
  • a virtual table
  • a copy of a table
  • a pointer to a table
  • query complexity
  • search time for certain types of queries
  • the amount of storage used in searching a database
  • the design effort needed for developing the database search algorithms
  • foreign
  • hash
  • national
  • primary
  • Hierarchical
  • Network
  • Relational
  • Theoretical
  • Internal view
  • External view
  • Network schema
  • Data management language
  • An E-R diagram is transformed into the database design.
  • An E-R diagram is transformed into the internal database.
  • An E-R diagram is transformed into the physical database.
  • An E-R diagram is used to represent the database requirements.
  • Cardinality
  • Optionality
  • Disjointedness
  • Referential integrity
  • Attributes
  • Constraints
  • Entities
  • Relations
  • σcourse_id=”CS403″ (course)
  • σdescription=”CS403″ (course)
  • σcourse_id = “CS403” (registration)
  • σdescription=”CS403″ (registration)
  • The mathematical basis for SQL
  • A language used to store data in a database
  • The translation of a query by an SQL compiler
  • An informal way to describe how a database works
  • An equijoin
  • A hash join
  • A theta join
  • Aa natural join
  • A theta join
  • An inner join
  • A natural join
  • A cartesian product
Modern Database Systems Saylor Academy 1
  • First normal form
  • Second normal form
  • Third normal form
  • Boyce-Codd normal form
  • It has no identical rows
  • It has no repeating groups
  • It has no partial functional dependencies
  • It has no transitive functional dependencies
Part No.DescriptionCust IDNameQuantity Ordered
2361Pens7810J. Smith22
2371Paper Clips7810J. Smith1000
2914Pens7914K. Jones900
  • A deletion anomaly
  • An update anomaly
  • An insertion anomaly
  • A modification anomaly
Part No.DescriptionCust IDNameQuantity Ordered
2361Pens7810J. Smith22
2371Paper Clips7810J. Smith1000
2914Pens7914K. Jones900
  • A hash anomaly
  • A deletion anomaly
  • An update anomaly
  • An insertion anomaly
  • Insert
  • Select
  • Update
  • Create table
  • Insert a new record into the table for each MA customer
  • Set the credit limit of all MA customers to credit limit * 1.05
  • Set the credit limit of all MA customers to credit limit / 1.05
  • Change state to MA where credit limit was increased by 5%
  • It can only be used by itself.
  • It can be included as part of an insert only.
  • It can be included as part of an update but not as part of a delete.
  • It can be included as part of an update, delete, or insert statement.
  • Query a table
  • Create a new table
  • Insert data into a table
  • Delete rows from a table
  • A listing of all customers
  • A listing of all customers with a credit limit greater than 500
  • A count of all customers
  • A count of all customers with a credit limit greater than 500
  • It is always the first part of an SQL select statement
  • It sorts the entire table by ordering the values of a column(s)
  • It sorts the result set from a query by ordering column values
  • It uses descending when the order for sorting is not specified
Registration
SIDCourse IDSem IDInstructorGrade
282712ENGL210201701H. ZacnyB+
362112CS101201701K. RossC
652123CS403201701K. RossA
362112CS403201701K. SmithC+
282712BIO101201601I. OlsenC
  • The entire table sorted by sid
  • No results due to an error
  • 282712 2

362112 2

652123 1

  • 282712 1

362112 1

652123 1

  • Full join
  • Left join
  • Half join
  • Empty join
Student
SIDLnameFnameMajor
986223SmithJanetChemistry
362112WilliamsHenryComputer Science
282712JonesJohnEnglish Literature
Registration
SIDCourse IDSem IDInstructorGrade
282712ENGL210201701H. ZacnyB+
362112CS101201701K. RossC
  • Williams CS101
  • Williams CS101 C

Jones ENGL210 B+

  • Smith null null

Williams CS101 C

Jones ENGL210

  • No output due to a syntax error
  • Cost = 10
  • Int X;

X = 10

  • int Dollars = 10;

Print Dollars

  • Int Dollars;

Dollars = 10;

Cost = Dollars

  • Distributed and leased
  • Centralized and leased
  • Centralized and distributed
  • Centralized and networked
  • Metadata
  • Abstract data
  • Semantic data
  • Referential data
  • Conceptual
  • External
  • Individual
  • Internal
  • constructs
  • constraints
  • foreign keys
  • primary keys
  • They have little vendor support
  • They integrate multiple paradigms
  • They cannot be easily implemented
  • They integrate incompatible paradigms
  • A model of a database
  • A design of a database
  • A diagram of a database
  • A specification of database requirements
Modern Database Systems Saylor Academy 2
  • An employee can work for many departments
  • An employee can work for one department and a department can only have one employee
  • An employee can work for one department and a department can have one or more employees
  • An employee can work for one department only and a department does not have to have employees
Modern Database Systems Saylor Academy 3
  • Imaging entity
  • Associative entity
  • Binary relationship
  • Ternary relationship
  • Π (Student)
  • Π (Course)
  • ΠName (Student)
  • σname (Student)
  • It is in fourth normal form
  • There are transitive dependencies
  • Every determinant is a candidate key
  • There are some partial functional dependencies
  • Add a new row to the course table
  • Delete all records in the course table
  • Delete three credit courses from the course table
  • Delete all records except those with three credits from the course table
  • Customers’ last and first names who live in a state that ends with an M
  • Customers’ last and first names who live in a state that begins with an M
  • a list of all customers in the table who live in a state that ends with an M
  • A list of all customers in the table who live in a state that begins with an M
  • Only joining tables
  • Choosing the correct table
  • Choosing the correct expression to display
  • Choosing only rows that fit a certain criterion
  • A list of all customer records with credit limit > 500 and < 1000
  • A list of all customer records with credit limit > =500 and < =1000
  • A list of (cid, lname, fname) where credit limit >500 and <1000
  • A list of (cid, lname, fname) where credit limit > =500 and < = 1000
  • Return all rows in all tables
  • Return only those rows with a matching row in the corresponding table
  • Return all rows in the right table regardless of whether there is a match in the left table
  • Return all rows in the left or right table regardless of whether there is a match in the corresponding table
Course
Course IDDescriptionCredits
CS101Computer Science I3
CS201Elementary Data Structures3
ENGL210Technical Writing3
Registration
SIDCourse IDSem IDInstructorGrade
282712ENGL210201701H. ZacnyB+
362112CS101201701K. RossC
 652123 CS403201603K. RossA
  • Technical Writing 28212 B+
  • Technical Writing 28212 B+

Computer Science I 362112 C

Null 652123 A

  • Technical Writing 28212 B+

Computer Science I 362112 C

Elementary Data Structures null null

  • Technical Writing 28212 B+

Computer Science I 362112 C

Null 652123 A

Elementary Data Structures null null

  • Letters
  • Data plus semantics
  • Letters that spell a word
  • A number and its data type
  • Low data quality
  • Limited data sharing
  • Short development time
  • Program-data independence
  • integrated and shared
  • redundant and accurate
  • functional and redundant
  • transactional and unconstrained
  • Data security
  • Data accuracy
  • Data redundancy
  • Data dependence
  • Primary keying
  • Check constraints
  • Referential integrity
  • Field-level validations
Modern Database Systems Saylor Academy 4
  • Primary key
  • Composite key
  • Multivalued attribute
  • Single-valued attribute
Modern Database Systems Saylor Academy 5
  • Unary
  • One to all
  • One to one
  • One to many
  • Entity integrity
  • Domain integrity
  • Semantic integrity
  • Referential integrity
  • The employee number of every staff employee and every instructor
  • Every employee who is either a member of the staff or an instructor
  • All tuples of the instructor relation along with all tuples of the staff relation
  • The employee number of all rows in the staff and in the instructor relations
Modern Database Systems Saylor Academy 6
  • First normal form
  • Third normal form
  • Fourth normal form
  • Boyce-Codd normal form
  • Delete table rows that match a condition
  • Truncate parts of a table that are too long
  • Remove table columns that match a condition
  • Insert a mark to indicate that a table is deleted
  • average
  • avg
  • mean
  • sqrt
  • Sort the results in group order
  • List the rows ordered by group
  • List a single row for each group
  • Insert new records into the table
  • A part of a database
  • A database application
  • An interface between applications
  • An interface between a database and users of the database
  • Network
  • Relational
  • Hierarchical
  • Object-oriented
  • Conceptual
  • Data
  • External
  • Internal
  • an index
  • a schema
  • a table
  • a view
  • Hierarchical
  • NoSQL
  • Object
  • Relational
  • Internal view
  • External view
  • Conceptual schema
  • Data management language
  • Data dependency
  • Limited vendor support
  • Low speed of operation
  • Structural independence
Modern Database Systems Saylor Academy 7
  • Name
  • License
  • SocSec#
  • StudentId
  • Circle
  • Diamond
  • Oval
  • Rectangle
  • Binary
  • Ternary
  • Tributary
  • Unary
Modern Database Systems Saylor Academy 8
Modern Database Systems Saylor Academy 9
Modern Database Systems Saylor Academy 10
Modern Database Systems Saylor Academy 11
Modern Database Systems Saylor Academy 12
Modern Database Systems Saylor Academy 13
Modern Database Systems Saylor Academy 14
Modern Database Systems Saylor Academy 15
Modern Database Systems Saylor Academy 16
Modern Database Systems Saylor Academy 17
  • Normalize the ERD until no more normalization can be done
  • Check that the DBMS can interpret the constraints in the ERD
  • Make sure that we have simplified the ERD as much as possible
  • Make sure that our ERD has only one entity and one relationship
  • They cannot be used in most modern database systems
  • They ensure values entered into a table satisfy conditions
  • They can only check numeric values for arithmetic relations
  • They cannot always be implemented in some SQL implementations
  • When there are no dependencies of an attribute on another attribute in the table
  • When there are no compound primary keys or partial key that can have a null value
  • When there are no partial dependencies or transitive dependencies on a primary key
  • When there are no repeating groups of values of an attribute in one or more columns
Part No.DescriptionCust IDNameQuantity Ordered
2361Pens7810J. Smith22
2371Paper Clips7810J. Smith1000
2914Pens7914K. Jones900
  • A non-normal form
  • A deletion anomaly
  • An update anomaly
  • An insertion anomaly
  • Foreign key
  • Primary key
  • Independent key
  • Functional dependency
  • It is used to create tables, views, etc., and data manipulation language is only used for inserts
  • It is used for queries and data manipulation language is used for updating and deleting tables only
  • It is used to create the files that store tables whereas data manipulation language is used to update data
  • It is used to create tables, indexes, etc., whereas data manipulation language is used to query the database
  • It is missing a left parenthesis
  • It is missing a right parenthesis
  • It is missing a value before the parenthesis
  • There are not enough fields in the statement
  • references, alias, check
  • null, primary key, view of
  • primary key, not null, unique
  • primary key, default, between
  • Customer data rows so that there are no duplicates
  • Customer rows so that there are no duplicate records
  • Customer rows so that there are no duplicate customers
  • Customer data rows so that there are no duplicate names
  • All last names and credit limits from 1000 to 2000, and 2000 to 3000
  • The last name of all customers with a credit limit of 1000, 2000, or 3000
  • All last names and credit limits where the credit limit is 1000, 2000, 3000
  • The last name of all customers with a credit from 1000 to 2000, and 2000 to 3000
Course
Course IDDescriptionCredits
CS101Computer Science I3
CS201Elementary Data Structures3
ENGL210Technical Writing3
Registration
SIDCourse IDSem IDInstructorGrade
282712ENGL210201701H. ZacnyB+
362112CS101201701K. RossC
  • Computer Science I 362112 C
  • Computer Science I 362112 C

Technical Writing 28212 B+

  • Computer Science I 362112 C

Elementary Data Structures null null

Technical Writing 28212 B+

  • No results due to syntax error
  • Network
  • Relational
  • Hierarchical
  • Object-oriented
  • Subschema
  • External view
  • Network schema
  • Data management language
  • Entity
  • Hierarchical
  • Network
  • Object
  • A field is not duplicated in the table
  • Two fields are related to one another
  • Field values are consistent in the table
  • There are no repeating groups in the table
  • All course descriptions and names of all students
  • Registered students and all the course descriptions
  • All student names and all courses that are registered
  • Registered students and descriptions of their courses
  • Join two or more tables based upon related fields
  • Join two or more tables based on related column fields
  • Join two or more columns together based on common names
  • Join two or more rows together based on related column fields

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