WGU C949 DATA
STRUCTURES AND
ALGORITHMS
[Document subtitle]
[DATE]
[COMPANY NAME]
[Company address]
, Certainly! Here are all the questions numbered:
1. A functions whose cost scales linearly with the size of the input O(n)
2. Iterating over a collection of data once often indicates an ______ algorithm. (alphabet for-
loop example) O(n)
3. A functions whose cost scales logarithmically with the input size O(log n)
4. Which type of function works by breaking down large problem into smaller and smaller
chunks? O(log n)
5. As the size of the input grows the cost of the algorithm does not increase at the same
rate. The overall cost of performing an operation on 1,000,000 items is only twice that of
performing the operation on 1,000 items. O(log n)
6. A function that exhibits quadratic growth relative to the input size O(n^2)
7. An example of this type of function is doubly nested loop O(n^2)
8. Which type of function gets really expensive really quickly? O(n^2)
9. A function that has two inputs that contribute to growth O(nm)
10. An example of this type of function is when there is a nested loop that iterates of two
distinct collections of data O(nm)
11. Are Big-O cases used in the best or worst situations? Worst
12. Which statement is static? readonly Contact[] contacts = new Contact[];
STRUCTURES AND
ALGORITHMS
[Document subtitle]
[DATE]
[COMPANY NAME]
[Company address]
, Certainly! Here are all the questions numbered:
1. A functions whose cost scales linearly with the size of the input O(n)
2. Iterating over a collection of data once often indicates an ______ algorithm. (alphabet for-
loop example) O(n)
3. A functions whose cost scales logarithmically with the input size O(log n)
4. Which type of function works by breaking down large problem into smaller and smaller
chunks? O(log n)
5. As the size of the input grows the cost of the algorithm does not increase at the same
rate. The overall cost of performing an operation on 1,000,000 items is only twice that of
performing the operation on 1,000 items. O(log n)
6. A function that exhibits quadratic growth relative to the input size O(n^2)
7. An example of this type of function is doubly nested loop O(n^2)
8. Which type of function gets really expensive really quickly? O(n^2)
9. A function that has two inputs that contribute to growth O(nm)
10. An example of this type of function is when there is a nested loop that iterates of two
distinct collections of data O(nm)
11. Are Big-O cases used in the best or worst situations? Worst
12. Which statement is static? readonly Contact[] contacts = new Contact[];
