WGU C949 Algorithms & Data Structures
Frequently Tested Exam Questions With
Verified Multiple Choice and Conceptual
Actual 100% Correct Detailed Answers
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1. O(N^2) - ANSWER Insertion Sort Time Complexity
2. Merge Sort - ANSWER A sort type that uses recursion to break list in
half, sorts the halves, then combines them again.
3. O(N Log N) - ANSWER Merge Sort Time Complexity
4. Quicksort - ANSWER A sort type that partitions list into high and low by
using a midpoint and pivot value. Then sorts the partitions.
5. O(N^2) - ANSWER Quicksort Time Complexity
6. X + ((Y-X)/2) - ANSWER Function for finding midpoint in a quicksort.
7. Midpoint = index, pivot = actual value - ANSWER Describe midpoints
and pivot values of a quicksort.
,8. Heap Sort - ANSWER A sort type that build a tree and repeatedly
extracts the max.
9. O(N log N) - ANSWER Heap Sort Time Complexity
10.Radix Sort - ANSWER A sort type that sorts by hashing values to buckets
based on individual digits such as the 1's place, then combines into array,
then hashes again using the 10's, 100's, etc.
11.O(nk) where K = number of digits in largest number. - ANSWER Radix
Sort Time Complexity
12.Shell Sort - ANSWER Generalization of insertion sort but also uses a gap
value. Sorts elements farther apart and reduces the gap.
13.O(N^2) - ANSWER Time Complexity of Shell Sort
14.Bubble, Selection, Insertion - ANSWER Sort types good for small
datasets.
15.Merge, Quicksort, Heap - ANSWER Sort types better for larger datasets
16.Big O Notation - ANSWER Allows for comparing the efficiency of
algorithms.
, 17.O(1) - ANSWER The algorithm takes the same amount of time to
execute regardless of the size of the input. - Constant
18.O(Log N) - ANSWER The runtime increases logarithmically as the input
size increases. Typically occurs in algorithms that halve the problem size at
each step, like binary search.
19.O(N log N) - ANSWER The runtime increases more than linearly but less
than quadratically. Common in efficient sorting algorithms like merge sort
and quicksort. Efficient for large datasets
20.O(N^2) - ANSWER The runtime increases quadratically with the size of
the input. If you double the input size, the runtime quadruples.
21.O(2^N) - ANSWER The runtime doubles with each additional element in
the input. Common in algorithms that solve problems by brute force or
explore all possible solutions. - Exponential
22.O(2n) simplifies to O(n) - ANSWER What does O(2n) simplify to?
23.O(n2+n) simplifies to O(n2). Focus on dominant term only. - ANSWER
What does O(N^2 + n) simplify to?
24.O(n+10) simplifies to O(n) - ANSWER What does O(N +10) simplify to?
Frequently Tested Exam Questions With
Verified Multiple Choice and Conceptual
Actual 100% Correct Detailed Answers
Guaranteed Pass!!Current Update!!
1. O(N^2) - ANSWER Insertion Sort Time Complexity
2. Merge Sort - ANSWER A sort type that uses recursion to break list in
half, sorts the halves, then combines them again.
3. O(N Log N) - ANSWER Merge Sort Time Complexity
4. Quicksort - ANSWER A sort type that partitions list into high and low by
using a midpoint and pivot value. Then sorts the partitions.
5. O(N^2) - ANSWER Quicksort Time Complexity
6. X + ((Y-X)/2) - ANSWER Function for finding midpoint in a quicksort.
7. Midpoint = index, pivot = actual value - ANSWER Describe midpoints
and pivot values of a quicksort.
,8. Heap Sort - ANSWER A sort type that build a tree and repeatedly
extracts the max.
9. O(N log N) - ANSWER Heap Sort Time Complexity
10.Radix Sort - ANSWER A sort type that sorts by hashing values to buckets
based on individual digits such as the 1's place, then combines into array,
then hashes again using the 10's, 100's, etc.
11.O(nk) where K = number of digits in largest number. - ANSWER Radix
Sort Time Complexity
12.Shell Sort - ANSWER Generalization of insertion sort but also uses a gap
value. Sorts elements farther apart and reduces the gap.
13.O(N^2) - ANSWER Time Complexity of Shell Sort
14.Bubble, Selection, Insertion - ANSWER Sort types good for small
datasets.
15.Merge, Quicksort, Heap - ANSWER Sort types better for larger datasets
16.Big O Notation - ANSWER Allows for comparing the efficiency of
algorithms.
, 17.O(1) - ANSWER The algorithm takes the same amount of time to
execute regardless of the size of the input. - Constant
18.O(Log N) - ANSWER The runtime increases logarithmically as the input
size increases. Typically occurs in algorithms that halve the problem size at
each step, like binary search.
19.O(N log N) - ANSWER The runtime increases more than linearly but less
than quadratically. Common in efficient sorting algorithms like merge sort
and quicksort. Efficient for large datasets
20.O(N^2) - ANSWER The runtime increases quadratically with the size of
the input. If you double the input size, the runtime quadruples.
21.O(2^N) - ANSWER The runtime doubles with each additional element in
the input. Common in algorithms that solve problems by brute force or
explore all possible solutions. - Exponential
22.O(2n) simplifies to O(n) - ANSWER What does O(2n) simplify to?
23.O(n2+n) simplifies to O(n2). Focus on dominant term only. - ANSWER
What does O(N^2 + n) simplify to?
24.O(n+10) simplifies to O(n) - ANSWER What does O(N +10) simplify to?
