2.3.1 Algorithms

When designing an algorithm, what is the first crucial step?

• Writing the code.

• Defining the problem and its requirements.

• Testing the output.

• Optimising for speed.

What does algorithm analysis involve?

• Writing code without testing.

• Evaluating the efficiency and correctness of an algorithm.

• Ignoring the problem's complexity.

• Only focusing on the user interface.

What factor primarily determines the suitability of an algorithm for a given task?

• The colour of the code.

• The algorithm's efficiency in terms of time and space.

• The programmer's preference.

• The complexity of the user interface.

Which algorithm is generally more suitable for a large, unsorted dataset when speed is crucial?

• Linear search.

• Binary search.

• Bubble sort.

• Quick sort.

When would a linear search be more suitable than a binary search?

• When the data is sorted.

• When the data is unsorted.

• When the data is very large.

• When the data is in a tree structure.

What does Big O notation describe?

• The exact execution time of an algorithm.

• The upper bound of an algorithm's growth rate.

• The programming language used.

• The user interface complexity.

What does O(1) represent?

• Linear complexity.

• Constant complexity.

• Polynomial complexity.

• Exponential complexity.

What does O(n) represent?

• Logarithmic complexity.

• Linear complexity.

• Exponential complexity.

• Constant complexity.

What does O(n^2) represent?

• Logarithmic complexity.

• Linear complexity.

• Polynomial complexity.

• Exponential complexity.

What does O(2^n) represent?

• Logarithmic complexity.

• Linear complexity.

• Polynomial complexity.

• Exponential complexity.