Computational Thinking

What is a key feature that makes a problem solvable by computational methods?

• The problem can only be solved manually

• The problem can be solved within a finite, realistic amount of time

• The problem requires infinite resources

• The problem does not have any inputs or outputs

What is the first step in recognizing a computable problem?

• Testing the problem with pre-coded modules

• Assigning the problem to a software development team

• Identifying whether the problem can be solved using an algorithm

• Decomposing the problem into smaller parts

What technique involves breaking a problem down into smaller, more manageable parts?

• Data mining

• Problem decomposition

• Performance modelling

• Heuristics

Which problem-solving strategy involves halving the size of the problem with every iteration?

• Use of abstraction

• Pipelining

• Backtracking

• Use of divide and conquer

What is the purpose of using abstraction in problem-solving?

• To divide the problem into equal halves

• To increase the complexity of the problem

• To simplify a problem by removing excessive details

• To make the problem unsolvable

Which technique is used to identify patterns in large sets of data?

• Divide and conquer

• Backtracking

• Data mining

• Visualisation

What is a characteristic of heuristics in problem-solving?

• They are mainly used for small data sets

• They eliminate the need for algorithms

• They are used when the standard solution is unreasonably time-consuming

• They provide a perfectly accurate solution

What is the main advantage of performance modelling?

• It requires true performance testing

• It provides a cheaper and less time-consuming method of testing

• It is only used in non-critical systems

• It increases the complexity of testing

What is the principle behind pipelining in software development?

• The output of one process becomes the input of another

• Breaking the problem into smaller parts that are unsolvable

• Using pre-coded modules to increase development time

• Ignoring the core aspects of the solution

How does visualisation aid in understanding data?

• By reducing the amount of data available

• By presenting data in a way that is harder to understand

• By making it more complex

• By identifying trends that were not otherwise obvious