WGU C949 – DATA STRUCTURES AND ALGORITHMS
OBJECTIVE ASSESSMENT | OA V1 AND V2
COMPREHENSIVE QUESTIONS AND ANSWERS WITH
DETAILED SOLUTIONS – NEWLY UPDATED FOR 2026
GUARANTEED SUCCESS
Section 1: Algorithm Analysis & Big-O (1–20)
1. What does Big-O notation describe?
Answer: The upper bound (worst-case) time or space complexity of an algorithm.
2. Which Big-O represents constant time? Answer: O(1)
3. What is the time complexity of binary search? Answer:
O(log n)
4. Which algorithm has O(n²) complexity in its worst case?
Answer: Bubble sort
5. What is the best-case time complexity of insertion sort?
Answer: O(n)
6. What does O(n log n) commonly represent?
Answer: Efficient comparison-based sorting algorithms (e.g., merge sort)
7. Which is faster as n grows large: O(n) or O(log n)?
Answer: O(log n)
8. What is the worst-case time complexity of quicksort?
Answer: O(n²)
9. What is the average-case time complexity of quicksort?
Answer: O(n log n)
10. Which Big-O ignores constants and lower-order terms?
Answer: Asymptotic notation
11. What is space complexity?
Answer: Memory required by an algorithm as input size grows
12. What is the time complexity of accessing an array
element by index? Answer: O(1)
13. What does exponential time complexity look like?
Answer: O(2ⁿ)
14. Which complexity is most efficient for large inputs?
Answer: O(1)
, 15. What is the time complexity of traversing a linked list?
Answer: O(n)
16. Which notation describes best-case complexity? Answer:
Ω (Omega)
17. Which notation describes average-case complexity?
Answer: Θ (Theta)
18. What happens to runtime when input size doubles in
O(n²)?
Answer: Runtime increases by a factor of four
19. Which algorithm has O(n log n) in all cases? Answer:
Merge sort
20. What is amortized analysis used for? Answer: Averaging
cost of operations over time
Section 2: Arrays & Strings (21–35)
21. What is an array?
Answer: A contiguous block of memory storing elements of the same type
22. What is the time complexity of inserting into the middle
of an array? Answer: O(n)
23. What is the advantage of arrays? Answer: Fast indexed
access
24. What is the main limitation of arrays? Answer: Fixed size
25. What is a dynamic array?
Answer: An array that resizes automatically (e.g., ArrayList)
26. What happens when a dynamic array resizes? Answer:
Elements are copied to a larger array
27. What is string immutability?
Answer: Strings cannot be changed after creation
28. Why is string concatenation expensive? Answer: It creates
new objects
29. Which structure is best for frequent string modifications?
Answer: StringBuilder (or equivalent)
OBJECTIVE ASSESSMENT | OA V1 AND V2
COMPREHENSIVE QUESTIONS AND ANSWERS WITH
DETAILED SOLUTIONS – NEWLY UPDATED FOR 2026
GUARANTEED SUCCESS
Section 1: Algorithm Analysis & Big-O (1–20)
1. What does Big-O notation describe?
Answer: The upper bound (worst-case) time or space complexity of an algorithm.
2. Which Big-O represents constant time? Answer: O(1)
3. What is the time complexity of binary search? Answer:
O(log n)
4. Which algorithm has O(n²) complexity in its worst case?
Answer: Bubble sort
5. What is the best-case time complexity of insertion sort?
Answer: O(n)
6. What does O(n log n) commonly represent?
Answer: Efficient comparison-based sorting algorithms (e.g., merge sort)
7. Which is faster as n grows large: O(n) or O(log n)?
Answer: O(log n)
8. What is the worst-case time complexity of quicksort?
Answer: O(n²)
9. What is the average-case time complexity of quicksort?
Answer: O(n log n)
10. Which Big-O ignores constants and lower-order terms?
Answer: Asymptotic notation
11. What is space complexity?
Answer: Memory required by an algorithm as input size grows
12. What is the time complexity of accessing an array
element by index? Answer: O(1)
13. What does exponential time complexity look like?
Answer: O(2ⁿ)
14. Which complexity is most efficient for large inputs?
Answer: O(1)
, 15. What is the time complexity of traversing a linked list?
Answer: O(n)
16. Which notation describes best-case complexity? Answer:
Ω (Omega)
17. Which notation describes average-case complexity?
Answer: Θ (Theta)
18. What happens to runtime when input size doubles in
O(n²)?
Answer: Runtime increases by a factor of four
19. Which algorithm has O(n log n) in all cases? Answer:
Merge sort
20. What is amortized analysis used for? Answer: Averaging
cost of operations over time
Section 2: Arrays & Strings (21–35)
21. What is an array?
Answer: A contiguous block of memory storing elements of the same type
22. What is the time complexity of inserting into the middle
of an array? Answer: O(n)
23. What is the advantage of arrays? Answer: Fast indexed
access
24. What is the main limitation of arrays? Answer: Fixed size
25. What is a dynamic array?
Answer: An array that resizes automatically (e.g., ArrayList)
26. What happens when a dynamic array resizes? Answer:
Elements are copied to a larger array
27. What is string immutability?
Answer: Strings cannot be changed after creation
28. Why is string concatenation expensive? Answer: It creates
new objects
29. Which structure is best for frequent string modifications?
Answer: StringBuilder (or equivalent)
