Big O notation is used to describe:

• The exact runtime of an algorithm

• The growth rate of an algorithm as input size increases

• The memory size of a program

• The programming language speed

Which of the following represents the fastest time complexity?

• O(1)

• O(log n)

• O(n)

• O(n²)

If an algorithm takes the same amount of time regardless of input size, its time complexity is:

• O(n)

• O(n²)

• O(1)

• O(log n)

Which of the following is better in terms of efficiency for large input sizes?

• O(n²)

• O(log n)

• O(2ⁿ)

• O(n³)

The time complexity of inserting an element at the end of an array is usually:

• O(1)

• O(n)

• O(n²)

• O(log n)

The Big O of binary search is:

• O(1)

• O(n)

• O(log n)

• O(n²)

The nested loop below has what time complexity? for i in range(n): for j in range(n): print(i, j)

• O(n)

• O(n²)

• O(log n)

• O(n log n)

Which one is not a valid Big O notation?

• O(n³)

• O(1)

• O(5n + 10)

• O(n + log n)

If an algorithm has time complexity O(n), doubling the input size will roughly:

• Double the work

• Square the work

• Halve the work

• Not change the work

The Big O of a constant-time algorithm is:

• O(1)

• O(n)

• O(n²)

• O(log n)