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Question 1
Which of the following best represents the time complexity of accessing an element in an array by its index?
O(1)
O(n)
O(log n)
O(n^2)
Question 2
What is the time complexity of a nested loop where the outer loop runs n times and the inner loop runs m times?
O(n)
O(m)
O(n * m)
O(n + m)
Question 3
Consider an algorithm that takes an array of size n and performs a loop from 1 to n^2. What is the time complexity of this algorithm?
O(n)
O(n2)
O(n3)
O(n4)
Question 4
Which of the following is true about the Big O notation (O(f(n)))?
It describes the lower bound of the runtime of an algorithm.
It describes the exact runtime of an algorithm.
It describes the upper bound of the runtime of an algorithm.
It describes the average runtime of an algorithm.
Question 5
Which of the following options correctly matches the notation with the parameters they define
Big-O Notation (O-notation) : Average Case complexity
Omega Notation (Ω-notation) : Worst Case complexity
Theta Notation (Θ-notation) : Best Case complexity
Big-O Notation (O-notation) : Best Case complexity
Omega Notation (Ω-notation) : Average Case complexity
Theta Notation (Θ-notation) : Worst Case complexity
Big-O Notation (O-notation) : Worst Case complexity
Omega Notation (Ω-notation) : Average Case complexity
Theta Notation (Θ-notation) : Best Case complexity
Big-O Notation (O-notation) : Worst Case complexity
Omega Notation (Ω-notation) : Best Case complexity
Theta Notation (Θ-notation) : Average Case complexity
Question 6
If an algorithm’s time complexity is O(n^2), which of the following best describes its performance when the input size is doubled?
The time taken will double.
The time taken will remain the same.
The time taken will increase by a factor of 2^2.
The time taken will quadruple.
Question 7
What does O(n) time complexity indicate in an algorithm?
The algorithm's execution time grows exponentially with the input size.
The algorithm's execution time grows linearly with the input size.
The algorithm’s execution time does not depend on the input size.
The algorithm’s execution time is constant, irrespective of the input size.
Question 8
Determine the time complexity for the following recursive function :
O(n)
O(log n)
O(2^n)
O(n^2)
Question 9
What is the time complexity of the following recursive function?
Note : The merge function takes linear time.
O(log n)
O(n log n)
O(n^2)
O(n)
Question 10
Consider a recursive function with a branching factor of 3 and a depth of recursion of n. What would be the time complexity of this recursion?
O(3n)
O(n3)
O(3n)
O(n2)
There are 17 questions to complete.
