WGU C949 DATA STRUCTURES AND ALGORITHMS EXAM | 105 COMPLETE
QUESTIONS WITH EXPERT SOLUTIONS | 2026 LATEST UPDATED | GET A+
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[];
readonly Contact contacts = new Contacts[100];: readonly Contact contacts =
new Contacts[100];
13. A container where data is stored in nodes consisting of a single data
item and a reference to the next node: Linked List
14. A ______ is a container where nodes of data are linked together into a
list: Linked List
15. Linking together complex nodes into a single structure: Linked List
16. Each link in a chain for a linked lists is called a ______: node
QUESTIONS WITH EXPERT SOLUTIONS | 2026 LATEST UPDATED | GET A+
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[];
readonly Contact contacts = new Contacts[100];: readonly Contact contacts =
new Contacts[100];
13. A container where data is stored in nodes consisting of a single data
item and a reference to the next node: Linked List
14. A ______ is a container where nodes of data are linked together into a
list: Linked List
15. Linking together complex nodes into a single structure: Linked List
16. Each link in a chain for a linked lists is called a ______: node
