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CSE 511 Midterm UPDATED Exam Questions and CORRECT Answers

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CSE 511 Midterm UPDATED Exam Questions and CORRECT Answers Big data - CORRECT ANSWER -volume of available data is huge. data keeps growing at staggering rate. data comes from variety of sources in totally different formats Scalable data processing allows database processing systems to cope with the _______, ________, and _________ aspects that big data brings into the system - CORRECT ANSWER -volume, velocity, variety

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CSE 511 Midterm UPDATED Exam
Questions and CORRECT Answers
Big data - CORRECT ANSWER -volume of available data is huge. data keeps growing at
staggering rate. data comes from variety of sources in totally different formats


Scalable data processing allows database processing systems to cope with the _______,
________, and _________ aspects that big data brings into the system - CORRECT
ANSWER -volume, velocity, variety


Best data processing system for operational workload (bank, online store, etc) - CORRECT
ANSWER -Relational DBMS (Centralized, distributed)


Unstructured data (highly available systems) data processing system - CORRECT
ANSWER -NoSQL, graph, document, key-value stores)



Analytics over massive-scale data (data processing system) - CORRECT ANSWER -
Hadoop, Spark, Kafka, GraphX


Database - CORRECT ANSWER -large, integrated collection of data



3 main functions of DBMS - CORRECT ANSWER -store, retrieve, manage



Store (DBMS function) - CORRECT ANSWER -persist and maintain data in storage
systems with structure


Retrieve (DBMS function) - CORRECT ANSWER -define the way of use/read the data by
the upper layer applications

,Manage (DBMS function) - CORRECT ANSWER -way of data being managed and
manipulated


Benefits of DBMS - CORRECT ANSWER -Data independence, efficient data access, data
integrity and security, data administration, concurrent access and crash recovery, reduced app
development time


Data Model - CORRECT ANSWER -collection of concepts for describing data



schema - CORRECT ANSWER -description of a particular collection of data, using given
data model. ex. students(sid: string, name: string, GPA: float)


Relational Model - CORRECT ANSWER -relations shown with rows and columns. most
widely used model


External schema - CORRECT ANSWER -view describes how users see the data . Ex.
Course_info(cid:string, enrollment:integer)


conceptual schema - CORRECT ANSWER -defines logical structure of the collection of
data AKA logical. Ex (three tables Students(), Course(), Enrolled())


physical schema - CORRECT ANSWER -describes how data are managed in storage
systems. Ex. relations stored as unordered files, index on first column of students


Defines and creates schemas - CORRECT ANSWER -data description language (DDL)



Modifies/queries data (think language) - CORRECT ANSWER -Data Manipulation
language (DML)


Logical Date Independence - CORRECT ANSWER -Protection from changes in logical
structure of data

, Physical Data Independence - CORRECT ANSWER -Protection from physical structure
of data


2 parts of relation - CORRECT ANSWER -Instance, schema



instance - CORRECT ANSWER -a table, with rows and columns. # rows = cardinality, #
fields = degree/arity


SQL statement to create new relation - CORRECT ANSWER -CREATE TABLE Students
(sid CHAR(20),
name CHAR(20),
GPA REAL)


SQL statement to destroy relation - CORRECT ANSWER -DROP TABLE Students



SQL statement to alter schema - CORRECT ANSWER -ALTER TABLE Students
ADD COLUMN firstYear: integer


SQL statement to insert tuple - CORRECT ANSWER -INSERT INTO Students(sid, name,
login, age, gpa)
VALUE (...)


SQL statement to delete all tuples satisfying conditions - CORRECT ANSWER -DELETE
FROM Students S
WHERE S.name = "Smith"


Integrity constraints - CORRECT ANSWER -condition that must be true for any instance
of the database

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