Understanding Algorithm Complexity and Big O Notation (video)

What does Big O notation primarily describe?

• The color-coding of algorithm steps

• The scalability of an algorithm

• The difficulty of coding an algorithm

• The number of developers required

If an algorithm's complexity is described as 5 + 3n + 1, which part is most significant as n increases?

• 3n + 1

• 1

• 5

• 3n

According to Big O notation rules, what should be done with constants in complexity expressions?

• They should be the focus

• They should be ignored

• They should be added

• They should be multiplied

What is the time complexity of an algorithm that involves a single for-loop iterating over input?

• O(1)

• O(n log n)

• O(n)

• O(n²)

Which Big O classification describes an algorithm that doubles execution time with each additional input item?

• O(2ⁿ)

• O(n)

• O(log n)

• O(n²)

What is the complexity of a nested loop where the inner loop runs n times for each iteration of the outer loop?

• O(n)

• O(log n)

• O(n + n)

• O(n²)

How does the complexity O(1) behave as input size increases?

• Increases exponentially

• Remains constant

• Decreases

• Increases linearly

What is the Big O notation for an algorithm that takes the same amount of time regardless of input size?

• O(n)

• O(1)

• O(log n)

• O(n²)

In a scenario with multiple operations, how is overall complexity determined according to Big O notation?

• By adding complexities of all operations

• By averaging the complexities

• By taking the complexity of the most significant operation

• By multiplying the complexities

Which complexity class should be avoided due to rapid increase in execution time with additional inputs?

• Exponential

• Polynomial

• Logarithmic

• Linear