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DSA midterm review Questions and Answers 2024
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Exam of 8 pages for the course DSA at DSA (DSA midterm review)
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DSA midterm reviewWhat is the worst case computational complexity of the following code snippet in terms
of Big O notation?
int sum = 0
for (int i=0; i<n; i++)
for (int j=0; j<i; j++)
sum = sum+j; - answerO(n^2) (n squared)
What is the computational complexity of the following code snippet?
int result = 0
for (int i = 0; i < n; i++)
for (int j = i; j > 0; j--)
result += 1; - answerO(n^2)
What is the worst case computational complexity of the following code snippet in terms
of Big O notation?
int x = 1
while (x < n)
x *= 2 - answerO(log n)
What is the worst case computational complexity of the following code snippet in terms
of Big O notation?
result = 0
for (int i = 0; i < n; i++)
result += i;
for (int j = 1; j < m; j *= 2)
result *= j; - answerO(n + log m)
Which of the following functions T(n), belongs to the family of O(n^3*(log2n)) -
answern^3*(log2(log2n))
n^2+n+5000
1000000
n^3
n^3*(log3n)
, What is the worst case computational complexity of the following code snippet in terms
of Big O notation?
result = 0
for (i=0; i<10; i++)
for (j=0; j<i; j++)
result += i*j; - answerO(1)
An algorithm's runtime is given by T(n, m) = 3m^3+4m^3*log2m+3n^2+n+100. -
answerO(m^3*log2(m)+n^2)
Which family/families does the following function T(n) = n^5*log2(n) belong to? Check
all that apply. - answerΩ(10000)
O(2000n^5+2000^n)
O(n^5log3(n))
Which of the following are FALSE? Select all that apply. - answer-The best case time
complexity of linear search is O(1) and occurs when there is just one element in an
array
-If the growth rate for algorithm A can be represented by T(n) = n and the growth rate for
algorithm B can be represented by U(n) = log(n) we can say that algorithm A is faster
than algorithm B.
Examine the following code snippets below and determine which has a slower growth
rate. Consider "c" to be a positive integer constant (c > 1) :
Snippet A:
for(int i = 0; i < n; i++){
for(int j = n; j > 0; j /= c){
print("Hello");}}
Snippet B:
for(int i = 0; i < n; i++){
for(int j = n; j >= 100; j--){
print("Hello");}} - answerSnippet A has a slower growth rate than Snippet B
Answer: Snippet A will grow at f(n) = n*logcnSnippet B will grow at g(n) = n*n = n2. So,
A will have a slower growth rate
What is the computational complexity of adding an item to a Queue in the worst case in
terms of Big O notation? - answerO(1)
Consider the following operations on a stack
push(10);
push(5);
pop();
push(7);
pop();
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