Programming Algorithm and File Processing

In Python, which Big O notation describes an algorithm whose number of operations remains constant regardless of the input size?

• O(n)

• O(log n)

• O(1)

• O(n^2)

Big O efficiency focuses on the best-case scenario for a Python algorithm's performance.

• True

• False

What term describes how well a Python program would function if its dataset were significantly larger?

If a Python function contains a single for loop that iterates through all elements of a list, what is its Big O complexity?

• O(log n)

• O(1)

• O(n)

• O(n^2)

A Python algorithm that halves its input size with each iteration, such as a binary search, has a linear time complexity.

• True

• False

What Big O notation is typical for Python sorting algorithms like bubble sort and selection sort due to their nested loops?

• O(n^2)

• Quadratic

What is the primary purpose of calculating Big O notation for a Python algorithm?

• To determine the exact execution time.

• To analyze its memory usage.

• To analyze its scalability.

• To identify syntax errors.

In Python, what type of search algorithm checks each item in a data set sequentially to find information?

• Linear

• Linear search

For a Python binary search to be effective, the data set must be sorted.

• True

• False

Which of the following is a disadvantage of using a Python linear search algorithm for large data sets?

• It requires the data to be sorted.

• It has logarithmic time complexity.

• Its efficiency increases linearly with the number of elements.

• It uses too much memory.