Engineering Questions with Answers - Multiple Choice Questions

# Suffix Tree MCQ’s – 2

1 - Question

What is a time complexity for x pattern occurrence of length n?
a) O (log n!)
b) Ɵ (n!)
c) O (n2)
d) Ɵ (n + x)

Explanation: Suffix tree is also known as PAT tree or position tree. It allows fast string operation. To check if a substring is present in a string of a length of n, the time complexity for such operation is found to be O (n). The time complexity for x pattern occurrence of length n is Ɵ (n + x).

2 - Question

What is a time complexity for finding the longest substring that is common in string S1 and S2 (n1 and n2 are the string lengths of strings s1, s2 respectively)?
a) O (log n!)
b) Ɵ (n!)
c) O (n2+ n1)
d) Ɵ (n1 + n2)

Explanation: Suffix Tree allows fast string operation. To check if a substring is present in a string of a length of n, the time complexity for such operation is found to be O (n). The time complexity for finding the longest substring that is common in string S1 and S2 is Ɵ (n1 + n2).

3 - Question

What is a time complexity for finding the longest substring that is repeated in a string?
a) O (log n!)
b) Ɵ (n!)
c) O (n2+ n1)
d) Ɵ (n)

Explanation: Suffix Tree allows fast string operation. To check if a substring is present in a string of a length of n, the time complexity for such operation is found to be O (n). The time complexity for finding the longest substring that is repeated in a string is Ɵ (n).

4 - Question

What is a time complexity for finding frequently occurring of a substring of minimum length in a string?
a) Ɵ (n)
b) Ɵ (n!)
c) O (n2+ n1)
d) O (log n!)

Explanation: Suffix Tree allows fast string operation. To check if a substring is present in a string of a length of n, the time complexity for such operation is found to be O (n). The time complexity for finding frequently occurring of a substring of minimum length in a string is Ɵ (n).

5 - Question

What is a time complexity for finding the longest prefix that is common between suffix in a string?
a) Ɵ (n)
b) Ɵ (n!)
c) Ɵ (1)
d) O (log n!)

Explanation: Suffix Tree allows fast string operation. To check if a substring is present in a string of a length of n, the time complexity for such operation is found to be O (n). The time complexity for finding the longest prefix that is common between suffix in a string is Ɵ (1).

6 - Question

What is a time complexity for finding all the maximal palindrome in a string?
a) Ɵ (n)
b) Ɵ (n!)
c) Ɵ (1)
d) O (log n!)

Explanation: Palindrome is a string that is the same when reading forward as well as backward. To check if a substring is present in a string of a length of n, the time complexity for such operation is found to be O (n). The time complexity for finding all the maximal palindrome in a string is Ɵ (n).

7 - Question

What is a time complexity for finding all the tandem repeats?
a) Ɵ (n)
b) Ɵ (n!)
c) Ɵ (1)
d) O (n log n + z)

Explanation: Tandem Repeats are formed in DNA when the nucleotides pattern repeats more than once. To check if a substring is present in a string of a length of n, the time complexity for such operation is found to be O (n). The time complexity for finding all the tandem repeats in a string is O (n log n + z).

8 - Question

What is a time complexity for finding the longest palindromic substring in a string by using the generalized suffix tree?
a) Linear Time
b) Exponential Time
c) Logarithmic Time
d) Cubic Time

Explanation: Palindrome is a string that is same when reading forward as well as backward. The time complexity for finding the longest palindromic substring in a string by using generalized suffix tree is linear time.

9 - Question

Which of the following algorithm of data compression uses a suffix tree?
a) Weiner’s algorithm
b) Farach’s algorithm
c) Lempel – Ziv – Welch’s algorithm
d) Alexander Morse’s algorithm

Explanation: The concept of Suffix Tree was introduced by Weiner in 1973. Ukkonen provided the first online contribution of the Suffix tree. Farach gave the first suffix tree contribution for all alphabets in 1997. Lempel – Ziv – Welch’s algorithm of data compression uses a suffix tree.

10 - Question

Which of the following data clustering algorithm uses suffix tree in search engines?
a) Weiner’s algorithm
b) Farach’s algorithm
c) Lempel – Ziv – Welch’s algorithm
d) Suffix Tree Clustering

Explanation: The concept of Suffix Tree was introduced by Weiner in 1973. Ukkonen provided the first online contribution of Suffix. Farach gave the first suffix tree contribution for all alphabets in 1997. Suffix Tree Clustering is a data clustering algorithm that uses suffix tree in search engines.

11 - Question

What is a time complexity for finding the total length of all string on all edges of a tree?
a) Ɵ (n)
b) Ɵ (n!)
c) Ɵ (1)
d) O (n2)

Explanation: To check if a substring is present in a string of a length of n, the time complexity for such operation is found to be O (n). The time complexity for finding the total length of all string on all edges of a tree is O (n2).

12 - Question

Can suffix tree be used in string problems occurring in a text editor.
a) True
b) False

Explanation: It is a compressed search tree or prefix tree in which keys contain the suffix of text values as the text position. So, the suffix tree can be used in string problems occurring in a text editor. The time taken to solve the problem is linear to the length of the string.

13 - Question

Can suffix tree be used in bioinformatics problems and solutions.
a) True
b) False

Explanation: It is a compressed search tree or prefix tree in which keys contain the suffix of text values as the text position. So, a suffix tree is used in bioinformatics problems and solutions like pattern searching in DNA and protein sequences.

14 - Question

For what size of nodes, the worst case of usage of space in suffix tree seen?
a) n Nodes
b) 2n Nodes
c) 2n nodes
d) n! nodes

Explanation: In computer science, the worst case of usage of space in a suffix tree is found to be for a Fibonacci word for a full 2n nodes. The time complexity for usage of space is found to be O (n).

15 - Question

What is a time complexity for inserting an alphabet in the tree using hash maps?
a) O (log n!)
b) O (n!)
c) O (n2)
d) O (1)