Big-O Notation and Algorithm Efficiency Quiz

What does Big-O notation primarily measure?

• Space complexity
• Memory usage
• Algorithm correctness
• Time complexity

Which of the following is an example of a linear function?

• f(n) = 5n + 1
• f(n) = n² + 3n + 2
• f(n) = 2^n
• f(n) = log(n)

In the context of algorithms, what does 'cost' refer to?

• The financial cost of running the algorithm
• The complexity of the algorithm
• The time and space resources required
• The number of lines of code

Which of the following algorithms has a time complexity of O(n²)?

• Binary search
• Linear search
• Merge sort
• Bubble sort

What is the main advantage of using logarithmic functions in algorithms?

• They require less memory
• They are always faster than linear functions
• They are easier to implement
• They grow very slowly as input size increases

Which of the following statements about space complexity is true?

• It only considers the time taken to execute an algorithm
• It is irrelevant for small datasets
• It is always less important than time complexity
• It affects the number of algorithms that can run concurrently

What is the time complexity of an algorithm that sums numbers from 1 to n using a loop?

• O(1)
• O(n²)
• O(log n)
• O(n)

What is the primary operation in a binary search algorithm?

• Sorting the array
• Iterating through each element
• Comparing the midpoint item with the target item
• Calculating the average

What is the time complexity of an algorithm that prints all permutations of n items?

• O(n)
• O(n log n)
• O(n²)
• O(n!)

Which of the following best describes a quadratic function?

• f(n) = an² + bn + c
• f(n) = an + b
• f(n) = log(n)
• f(n) = 2^n