MS101 - Assignment Finals - (Reviewer)

What does time complexity measure?

• The amount of memory used by an algorithm

• The total number of lines in the code

• How execution time increases with input size

• The accuracy of an algorithm's output

Which of the following is an example of a time complexity class that grows fastest with input size?

• Linear (O(n))

• Logarithmic (O(log n))

• Constant (O(1))

• Exponential (O(2ⁿ))

What is the main focus of space complexity?

• The maximum time an algorithm can run

• The number of recursive calls

• The memory used by an algorithm

• The size of the output produced

In asymptotic notation, what does Big O represent?

• Exact running time

• Lower bound

• Upper bound

• Average case

Which type of time complexity describes the typical performance of an algorithm over all possible inputs?

• Best case

• Average case

• Worst case

• Standard case

What does Theta (Θ) notation represent?

• Worst-case performance

• Average-case time

• Tight bound on performance

• The slowest algorithm possible

Which time complexity class indicates performance that remains constant regardless of input size?

• O(n²)

• O(1)

• O(log n)

• O(n)

Which notation provides the lower bound of an algorithm's running time?

• O(n)

• Ω (Omega)

• Θ (Theta)

• o(n)

What does worst-case time complexity represent?

• Fastest scenario for the algorithm

• Typical running time for most inputs

• Maximum time on the largest or most difficult input

• Time complexity for random input

Why is analyzing time complexity important?

• To determine the color of the program interface

• To ensure the code looks clean

• To evaluate how well an algorithm scales with large input

• To count the number of functions used answer