Data Structures and Algorithms The Complete Masterclass - Space Complexity – Part 1

The video tutorial explains space complexity, which refers to the extra memory needed to run a program. It provides examples of constant space complexity (O(1)) and linear space complexity (O(N)), illustrating how space requirements change with input size. The tutorial concludes by summarizing the key points about space complexity.

What does space complexity refer to in a program?

• The complexity of the algorithm used

• The number of lines in a program

• The extra memory required to run a program

• The time taken to execute a program

In the first example, why is the space complexity considered O(1)?

• Because the program uses multiple variables

• Because the space required increases with input size

• Because only one extra space is needed regardless of input size

• Because the program does not use any extra space

How does the space complexity change in the second example when the number of inputs increases?

• It increases exponentially

• It increases linearly

• It decreases

• It remains constant

What is the space complexity of a program that stores results for each element in a list?

• O(log N)

• O(N^2)

• O(N)

• O(1)

Which of the following best describes a program with O(N) space complexity?

• The space required is constant regardless of input size

• The space required increases exponentially with input size

• The space required increases linearly with input size

• The space required decreases as input size increases