Quiz  /  Design and Analysis of Algorithms Class 12 MCQ Questions With...

  • 1. 
    How do you measure of the time complexity?

  • Big-O notation
  • Big-N notation
  • N notation
  • Small-O notation
  • Small-N notation

  • 2. 
    What does a constant time complexity mean?

  • The amount of time taken to complete an algorithm is independent to the number of inputted elements
  • The amount of time taken to complete an algorithm is independent from the number of elements inputted.
  • The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
  • The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
  • The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.

  • 3. 
    What does a linear time complexity mean?

  • The amount of time taken to complete an algorithm is independent from the number of elements inputted.
  • The amount of time taken to complete an algorithm is independent to the number of inputted elements
  • The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
  • The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
  • The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.

  • 4. 
    What is a logarithm?

  • How many times a certain number (base) is multiplied together to reach another number.
  • The space complexity is the amount of storage space an algorithm takes up
  • An algorithm is a series of steps that complete a task

  • 5. 
    What is meant by the time complexity of an algorithm?

  • The amount of time required to solve a particular problem
  • How difficult a problem is to solve
  • How many lines of code are required to solve a problem
  • How quickly a solution can be developed

  • 6. 
    What does a polynomial time complexity mean?

  • The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
  • The amount of time taken to complete an algorithm is independent to the number of inputted elements
  • The amount of time taken to complete an algorithm is independent from the number of elements inputted.
  • The amount of time taken to complete an algorithm is proportional to the power of 2 of the number of items inputted.
  • The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.

  • 7. 
    What is the Big-O notation good for?

  • It allows you to predict the amount of time taken to solve an algorithm given the number of items stored
  • The amount of time taken to complete an algorithm is independent from the number of elements inputted.
  • The amount of time taken to complete an algorithm is independent to the number of inputted elements
  • The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
  • The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.

  • 8. 
    What does the big-O notation show?

  • The effectiveness of an algorithm
  • The amount of time required to solve a particular problem
  • How difficult a problem is to solve
  • How many lines of code are required to solve a problem
  • How quickly a solution can be developed

  • 9. 
    What does an exponential time complexity mean?

  • The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
  • The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
  • The amount of time taken to complete an algorithm is independent to the number of inputted elements
  • The amount of time taken to complete an algorithm is independent from the number of elements inputted.
  • The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.

  • 10. 
    What is the Big-O notation of a linear search algorithm?

  • O(n)
  • O(log(n))
  • O(n2)

  • 11. 
    What is the Big-O notation of a binary search algorithm?

  • O(log(n))
  • O(n)
  • O(n2)

  • 12. 
    What does a logarithmic time complexity mean?

  • The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.
  • The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
  • The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
  • The amount of time taken to complete an algorithm is independent to the number of inputted elements
  • The amount of time taken to complete an algorithm is independent from the number of elements inputted.

  • 13. 
    What is space complexity?

  • The space complexity is the amount of storage space an algorithm takes up
  • How many times a certain number (base) is multiplied together to reach another number.
  • An algorithm is a series of steps that complete a task

  • 14. 
    How do you reduce the space complexity?

  • Try to complete all of the operations on the same data set
  • You reduce the amount of embedded for loops, and then reduce the amount of items you complete the operations on i.e. divide and conquer

  • 15. 
    How do you reduce the time complexity of an algorithm?

  • You reduce the amount of embedded for loops, and then reduce the amount of items you complete the operations on i.e. divide and conquer
  • Try to complete all of the operations on the same data set

  • 16. 
    What is the Big-O notation of a bubble sort algorithm?

  • O(n2)
  • O(log(n))
  • O(n)
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