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Worksheets2.3.1. Analysis Design and Comparison of Algorithms
Total questions: 18
Worksheet time: 18mins
Name
Class
Date
1.
What is meant by the time complexity of an algorithm?
a)
The amount of time required to solve a particular problem
b)
How difficult a problem is to solve
c)
How many lines of code are required to solve a problem
d)
How quickly a solution can be developed
2.
What two pieces of information allow you to analyse an algorithm?
a)
Time Complexity
b)
Space Complexity
c)
Size Complexity
d)
Complex Complexity
e)
Simplicity Complex
3.
How do you measure of the time complexity?
a)
Big-O notation
b)
Big-N notation
c)
N notation
d)
Small-O notation
e)
Small-N notation
4.
What does the big-O notation show?
a)
The effectiveness of an algorithm
b)
The amount of time required to solve a particular problem
c)
How difficult a problem is to solve
d)
How many lines of code are required to solve a problem
e)
How quickly a solution can be developed
5.
What is the Big-O notation good for?
a)
It allows you to predict the amount of time taken to solve an algorithm given the number of items stored
b)
The amount of time taken to complete an algorithm is independent from the number of elements inputted.
c)
The amount of time taken to complete an algorithm is independent to the number of inputted elements
d)
The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
e)
The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
6.
What does a linear time complexity mean?
a)
The amount of time taken to complete an algorithm is independent from the number of elements inputted.
b)
The amount of time taken to complete an algorithm is independent to the number of inputted elements
c)
The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
d)
The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
e)
The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.
7.
What does a constant time complexity mean?
a)
The amount of time taken to complete an algorithm is independent to the number of inputted elements
b)
The amount of time taken to complete an algorithm is independent from the number of elements inputted.
c)
The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
d)
The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
e)
The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.
8.
What does a polynomial time complexity mean?
a)
The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
b)
The amount of time taken to complete an algorithm is independent to the number of inputted elements
c)
The amount of time taken to complete an algorithm is independent from the number of elements inputted.
d)
The amount of time taken to complete an algorithm is proportional to the power of 2 of the number of items inputted.
e)
The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.
9.
What does an exponential time complexity mean?
a)
The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
b)
The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
c)
The amount of time taken to complete an algorithm is independent to the number of inputted elements
d)
The amount of time taken to complete an algorithm is independent from the number of elements inputted.
e)
The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.
10.
What does a logarithmic time complexity mean?
a)
The time taken to complete an algorithm will increase at a smaller rate as the number of elements inputted.
b)
The amount of time taken to complete an algorithm is proportional to 2 to the power of the number of items inputted.
c)
The amount of time taken to complete an algorithm is proportional to the number of items inputted to the power of n
d)
The amount of time taken to complete an algorithm is independent to the number of inputted elements
e)
The amount of time taken to complete an algorithm is independent from the number of elements inputted.
11.
What is a logarithm?
a)
How many times a certain number (base) is multiplied together to reach another number.
b)
The space complexity is the amount of storage space an algorithm takes up
c)
An algorithm is a series of steps that complete a task
12.
What is space complexity?
a)
The space complexity is the amount of storage space an algorithm takes up
b)
How many times a certain number (base) is multiplied together to reach another number.
c)
An algorithm is a series of steps that complete a task
13.
What is an algorithm?
a)
An algorithm is a series of steps that complete a task
b)
The space complexity is the amount of storage space an algorithm takes up
c)
How many times a certain number (base) is multiplied together to reach another number.
14.
How do you reduce the space complexity?
a)
Try to complete all of the operations on the same data set
b)
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?
a)
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
b)
Try to complete all of the operations on the same data set
16.
What is the Big-O notation of a linear search algorithm?
a)
O(n)
b)
O(log(n))
c)
O(n2)
17.
What is the Big-O notation of a binary search algorithm?
a)
O(log(n))
b)
O(n)
c)
O(n2)
18.
What is the Big-O notation of a bubble sort algorithm?
a)
O(n2)
b)
O(log(n))
c)
O(n)
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