Lesson 4.4.4.1

4.4.4.1 Comparing algorithms Quiz: AQA Computer Science, Unit 4

20 questions

Written by Revision Ninja · Last reviewed 11 October 2026

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Lesson 4.4.4.1, Comparing algorithms: 20 multiple choice questions for the AQA Computer Science (7517), Unit 4: Theory of computation, written with Revision Ninja.

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The 20 questions

  1. Algorithm complexity is expressed as a function relative to what key factor?

    • Memory capacity
    • Execution time
    • Problem size
    • Processor speed

  2. In which two respects can one algorithm be more efficient than another?

    • Time-wise and space-wise
    • Legally and commercially
    • Alphabetically and numerically
    • Visually and audibly

  3. Efficient algorithm implementation minimises execution time and what other resource?

    • Memory usage
    • Source code length
    • Disk capacity
    • Network bandwidth

  4. What are the two distinct types of algorithm efficiency?

    • Input and output
    • Time and space
    • Speed and bandwidth
    • Accuracy and precision

  5. What measure determines whether an algorithm is more efficient time-wise?

    • Lower memory usage
    • Smaller input size
    • Shorter execution time
    • Fewer code lines

  6. Algorithm A takes n² steps and B takes 100n steps. For n = 10, which uses fewer steps?

    • Neither algorithm
    • Both are equal
    • Algorithm A
    • Algorithm B

  7. For the same algorithms (n^2 and 100n), at what value of n do they perform equal numbers of steps?

    • 10
    • 100
    • 50
    • 1000

  8. For very large input sizes, which time complexity is more efficient?

    • O(n²)
    • O(2ⁿ)
    • O(n)
    • O(n!)

  9. For a large dataset, which sorting algorithm performs significantly fewer comparisons?

    • Merge sort
    • Insertion sort
    • Linear sort
    • Bubble sort

  10. Algorithm X uses O(n) memory and Y uses O(1) memory. Which is more space-efficient?

    • Algorithm Y
    • Both are equal
    • Algorithm X
    • Neither algorithm

  11. An O(n) algorithm takes 3 seconds on n items. Roughly how long does it take on 2n items?

    • 9 seconds
    • 6 seconds
    • 3 seconds
    • 12 seconds

  12. An O(n^2) algorithm takes 3 seconds on n items. Roughly how long does it take on 2n items?

    • 12 seconds
    • 6 seconds
    • 24 seconds
    • 9 seconds

  13. Why are algorithmic growth functions preferred over clock-time measurements?

    • Easier calculation
    • Code brevity
    • Exact execution time
    • Machine independence

  14. Which algorithm efficiency metric is independent of the hardware used?

    • Processor load
    • Power consumption
    • Number of steps
    • Clock time

  15. For large n, which complexity function represents a more efficient algorithm?

    • n²
    • n!
    • 5n + 20
    • 2ⁿ

  16. As input size grows, which rate of growth becomes impractical first?

    • Polynomial growth
    • Logarithmic growth
    • Linear growth
    • Exponential growth

  17. Roughly how many times more operations does bubble sort need than merge sort for one million items?

    • About 20
    • About 50,000
    • About 2
    • About 1,000,000

  18. Using extra memory to achieve faster execution time is an example of what trade-off?

    • Time-space trade-off
    • Input-output trade-off
    • Hardware-software trade-off
    • Accuracy-speed trade-off

  19. What resource advantage might a slower algorithm offer over a faster one?

    • Infinite stack size
    • Higher asymptotic growth
    • Lower time complexity
    • Lower memory usage

  20. What aspect of algorithm performance does Big-O notation describe?

    • Exact execution time
    • Long-run growth rate
    • Hardware clock speed
    • Memory bus width

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