SQA National 5 Computing Science Practice Exam

Algorithm efficiency refers to a measure that evaluates how the resources consumed by an algorithm—specifically time and space—scale with respect to the size of the input data. This concept is crucial in computer science as it helps in understanding how an algorithm will perform as the input size increases.

When discussing efficiency, time complexity deals with how the run time of an algorithm increases as the size of the input grows, often expressed using Big O notation (e.g., O(n), O(log n)). Space complexity, on the other hand, measures how the memory usage of an algorithm grows with the input size. Together, these two aspects provide a comprehensive picture of an algorithm's performance.

While options that involve speed might seem relevant, they often focus on a narrower aspect of efficiency. Similarly, calculating accuracy or benchmarking various algorithms, although important, do not address the broader concept of how effectively an algorithm utilizes resources in relation to input size. Therefore, efficiency encompasses both time and space considerations, thereby making option B the most accurate representation of algorithm efficiency.