# Data Structure MCQ - Complexity

This section focuses on the "Complexity" of the Data Structure. These Multiple Choice Questions (mcq) should be practiced to improve the Data Structure skills required for various interviews (campus interview, walk-in interview, company interview), placement, entrance exam and other competitive examinations.

1. Which of the following case does not exist in complexity theory?

A. Best case

B. Worst case

C. Average case

D. Null case

View Answer

Ans : D

Explanation:Null case does not exist in complexity Theory.

2. What is the time, space complexity of following code:

int a = 0, b = 0;
for (i = 0; i < N; i++)
{
a = a + rand();
}
for (j = 0; j < M; j++)
{
b = b + rand();
}

A. O(N * M) time, O(1) space

B. O(N + M) time, O(N + M) space

C. O(N + M) time, O(1) space

D. O(N * M) time, O(N + M) space

View Answer

Ans : C

Explanation:The first loop is O(N) and the second loop is O(M). Since we don’t know which is bigger, we say this is O(N + M). This can also be written as O(max(N, M)).

Since there is no additional space being utilized, the space complexity is constant / O(1).

3. The complexity of linear search algorithm is

A. O(n)

B. O(log n)

C. O(n2)

D. O(n log n)

View Answer

Ans : A

Explanation: The worst case complexity of linear search is O(n).

4.What is the time complexity of following code:

int a = 0;
for (i = 0; i < N; i++)
{
for (j = N; j > i; j--)
{
a = a + i + j;
}
}

A. O(N)

B. O(N*log(N))

C. O(N * Sqrt(N))

D. O(N*N)

View Answer

Ans : D

Explanation:= N + (N – 1) + (N – 2) + … 1 + 0

= N * (N + 1) / 2

= 1/2 * N^2 + 1/2 * N

O(N^2) times.

5. The Worst case occur in linear search algorithm when

A. Item is somewhere in the middle of the array

B. Item is not in the array at all

C. Item is the last element in the array

D. Item is the last element in the array or is not there at all

View Answer

Ans : D

Explanation:The Worst case occur in linear search algorithm when Item is the last element in the array or is not there at all.

6. What is the time complexity of following code:

int i, j, k = 0;
for (i = n / 2; i <= n; i++)
{
for (j = 2; j <= n; j = j * 2)
{
k = k + n / 2;
}
}

A. O(n)

B. O(nLogn)

C. O(n^2)

D. O(n^2Logn)

View Answer

Ans : B

Explanation:Let’s take the examples here.

for n = 16, j = 2, 4, 8, 16

for n = 32, j = 2, 4, 8, 16, 32

So, j would run for O(log n) steps.

i runs for n/2 steps.

So, total steps = O(n/ 2 * log (n)) = O(n*logn)

7. The worst case occur in quick sort when

A. Pivot is the median of the array

B. Pivot is the smallest element

C. Pivot is the middle element

D. None of the mentioned

View Answer

Ans : B

Explanation:This happens when the pivot is the smallest (or the largest) element. Then one of the partitions is empty, and we repeat recursively the procedure for N-1 elements.

8. What does it mean when we say that an algorithm X is asymptotically more efficient than Y?

A. X will always be a better choice for small inputs

B. X will always be a better choice for large inputs

C. Y will always be a better choice for small inputs

D. X will always be a better choice for all inputs

View Answer

Ans : B

Explanation: An algorithm X is said to be asymptotically better than Y if X takes smaller time than y for all input sizes n larger than a value n0 where n0 > 0.

9. The complexity of Fibonacci series is

A. O(2n)

B. O(log n)

C. O(n2)

D. O(n log n)

View Answer

Ans : A

Explanation:= Fibonacci is f(n) = f(n-1) + f(n-2), f(0) = 0, f(1) = 1. Let g(n) = 2n. Now prove inductively that f(n) > = g(n).

10. What is the time complexity of following code:

int a = 0, i = N;
while (i > 0)
{
a += i;
i /= 2;
}

A. O(N)

B. O(Sqrt(N))

C. O(N / 2)

D. O(log N)

View Answer

Ans : D

Explanation:We have to find the smallest x such that N / 2^x N
x = log(N).

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