COSC 2436 FINAL EXAM WITH ALL CORRECT & 100% VERIFIED ANSWERS |
LATEST VERSION |ALREADY GRADED A+
1. Which one of the following is correct
A tree data structure used for eflcient
about digital tree?
re-trieval of keys in a large dataset
2. Heap property says that every element of strings
of the tree is larger than any of its
True
descendants if they exists.
3. Which one of the following
statements are correct?
Child Node are the nodes that are
con-nected to a parent node. Child
nodes are at a lower level in the
hierarchy compared to their parent.
Leaf Node are a node with no
children. It is a terminal node in the
tree.
Internal Node is a node with at least
one child. Internal nodes are not
leaf nodes.
(CHILD---CHILDREN LOWER
LEVER- INTERNAL AT LEAST ONE
CHILD_
4. AVL Tree is a self-balancing binary search
tree True where the heights of the left
and right sub-trees of every node differ
by at most one.
5. Which of the following are the types algorithms that
of trees in Data Structure operate on or
manipulate data
6. Algorithm analysis involves structures. Data
evaluating the performance of
,COSC 2436 FINAL EXAM WITH ALL CORRECT & 100% VERIFIED ANSWERS |
LATEST VERSION |ALREADY GRADED A+
struc-tures are fundamental All of them
components of com-puter science
and are used to organize, store, and
manage data efficiently. Algorithm True
, COSC 2436 FINAL EXAM WITH ALL CORRECT & 100% VERIFIED ANSWERS |
LATEST VERSION |ALREADY GRADED A+
analysis focuses on understanding Expon
how the efficiency of algorithms is ential
affected by the un-derlying data time:
structures. O(2n)
Linear
7. Asymptotic analysis is a mathematical time:
nota-tion that is used to analyze the O(n)
time com-plexity and the runtime of Lineari
an algorithm for a large input. And it thmic
focuses on how the perfor-mance of an time:
algorithm scales with the size of the O(nlog
input data. It disregards constant n)
factors and lower-order terms, Quadra
focusing on the be-havior of the tic
algorithm as the input size ap- time:
proaches infinity. This analysis is O(n2)
crucial for understanding the
efficiency of algorithms for large
input sizes.
8. Space complexity refers to the
amount of memory space an algorithm
requires based on the size of the input
data. It also uses Big O notation to
express the upper bound of memory
consumption as the input size grows.
Space complexity includes both aux-
iliary space and space required by the
input.
9. 7 commonly used functions to describe
run-ning time are:
Constant time: O(1)
LATEST VERSION |ALREADY GRADED A+
1. Which one of the following is correct
A tree data structure used for eflcient
about digital tree?
re-trieval of keys in a large dataset
2. Heap property says that every element of strings
of the tree is larger than any of its
True
descendants if they exists.
3. Which one of the following
statements are correct?
Child Node are the nodes that are
con-nected to a parent node. Child
nodes are at a lower level in the
hierarchy compared to their parent.
Leaf Node are a node with no
children. It is a terminal node in the
tree.
Internal Node is a node with at least
one child. Internal nodes are not
leaf nodes.
(CHILD---CHILDREN LOWER
LEVER- INTERNAL AT LEAST ONE
CHILD_
4. AVL Tree is a self-balancing binary search
tree True where the heights of the left
and right sub-trees of every node differ
by at most one.
5. Which of the following are the types algorithms that
of trees in Data Structure operate on or
manipulate data
6. Algorithm analysis involves structures. Data
evaluating the performance of
,COSC 2436 FINAL EXAM WITH ALL CORRECT & 100% VERIFIED ANSWERS |
LATEST VERSION |ALREADY GRADED A+
struc-tures are fundamental All of them
components of com-puter science
and are used to organize, store, and
manage data efficiently. Algorithm True
, COSC 2436 FINAL EXAM WITH ALL CORRECT & 100% VERIFIED ANSWERS |
LATEST VERSION |ALREADY GRADED A+
analysis focuses on understanding Expon
how the efficiency of algorithms is ential
affected by the un-derlying data time:
structures. O(2n)
Linear
7. Asymptotic analysis is a mathematical time:
nota-tion that is used to analyze the O(n)
time com-plexity and the runtime of Lineari
an algorithm for a large input. And it thmic
focuses on how the perfor-mance of an time:
algorithm scales with the size of the O(nlog
input data. It disregards constant n)
factors and lower-order terms, Quadra
focusing on the be-havior of the tic
algorithm as the input size ap- time:
proaches infinity. This analysis is O(n2)
crucial for understanding the
efficiency of algorithms for large
input sizes.
8. Space complexity refers to the
amount of memory space an algorithm
requires based on the size of the input
data. It also uses Big O notation to
express the upper bound of memory
consumption as the input size grows.
Space complexity includes both aux-
iliary space and space required by the
input.
9. 7 commonly used functions to describe
run-ning time are:
Constant time: O(1)
