WGU C949 Data Structures and Algorithms I – Complete Study Guide, Exam Notes, Practice Questions, Algorithm Concepts, Coding Strategies, and Final Assessment Preparation for Western Governors University Students

WGU C949 Data Structures and Algorithms I – Complete
Study Guide, Exam Notes, Practice Questions, Algorithm
Concepts, Coding Strategies, and Final Assessment
Preparation for Western Governors University Students
Question 1: Which Big O notation best describes constant time complexity?
A. O(log n)
B. O(n)
C. O(1)
D. O(n^2)
CORRECT ANSWER: C. O(1)
Rationale: O(1) is the standard Big O notation that represents constant time complexity,
indicating how the runtime scales with input size.
Question 2: Which Big O notation best describes logarithmic time complexity?
A. O(n)
B. O(log n)
C. O(n^2)
D. O(1)
CORRECT ANSWER: B. O(log n)
Rationale: O(log n) is the standard Big O notation that represents logarithmic time complexity,
indicating how the runtime scales with input size.
Question 3: Which Big O notation best describes linear time complexity?
A. O(n log n)
B. O(1)
C. O(n)
D. O(n^2)
CORRECT ANSWER: C. O(n)
Rationale: O(n) is the standard Big O notation that represents linear time complexity, indicating
how the runtime scales with input size.
Question 4: Which Big O notation best describes linearithmic time complexity?
A. O(n)
B. O(n log n)
C. O(log n)
D. O(1)
CORRECT ANSWER: B. O(n log n)
Rationale: O(n log n) is the standard Big O notation that represents linearithmic time
complexity, indicating how the runtime scales with input size.
Question 5: Which Big O notation best describes quadratic time complexity?
A. O(n^3)
B. O(n)
C. O(n^2)
D. O(2^n)
CORRECT ANSWER: C. O(n^2)

,Rationale: O(n^2) is the standard Big O notation that represents quadratic time complexity,
indicating how the runtime scales with input size.
Question 6: Which Big O notation best describes cubic time complexity?
A. O(n!)
B. O(n^3)
C. O(n)
D. O(log n)
CORRECT ANSWER: B. O(n^3)
Rationale: O(n^3) is the standard Big O notation that represents cubic time complexity,
indicating how the runtime scales with input size.
Question 7: Which Big O notation best describes exponential time complexity?
A. O(n)
B. O(2^n)
C. O(n^2)
D. O(log n)
CORRECT ANSWER: B. O(2^n)
Rationale: O(2^n) is the standard Big O notation that represents exponential time complexity,
indicating how the runtime scales with input size.
Question 8: Which Big O notation best describes factorial time complexity?
A. O(n^2)
B. O(n)
C. O(n!)
D. O(2^n)
CORRECT ANSWER: C. O(n!)
Rationale: O(n!) is the standard Big O notation that represents factorial time complexity,
indicating how the runtime scales with input size.
Question 9: Which Big O notation best describes constant time complexity?
A. O(n)
B. O(n^2)
C. O(log n)
D. O(1)
CORRECT ANSWER: D. O(1)
Rationale: O(1) is the standard Big O notation that represents constant time complexity,
indicating how the runtime scales with input size.
Question 10: Which Big O notation best describes logarithmic time complexity?
A. O(1)
B. O(n)
C. O(log n)
D. O(n^2)
CORRECT ANSWER: C. O(log n)
Rationale: O(log n) is the standard Big O notation that represents logarithmic time complexity,
indicating how the runtime scales with input size.

,Question 11: Which Big O notation best describes linear time complexity?
A. O(1)
B. O(n)
C. O(n^2)
D. O(log n)
CORRECT ANSWER: B. O(n)
Rationale: O(n) is the standard Big O notation that represents linear time complexity, indicating
how the runtime scales with input size.
Question 12: Which Big O notation best describes linearithmic time complexity?
A. O(n)
B. O(1)
C. O(n^2)
D. O(n log n)
CORRECT ANSWER: D. O(n log n)
Rationale: O(n log n) is the standard Big O notation that represents linearithmic time
complexity, indicating how the runtime scales with input size.
Question 13: Which Big O notation best describes quadratic time complexity?
A. O(n)
B. O(n^2)
C. O(log n)
D. O(1)
CORRECT ANSWER: B. O(n^2)
Rationale: O(n^2) is the standard Big O notation that represents quadratic time complexity,
indicating how the runtime scales with input size.
Question 14: Which Big O notation best describes cubic time complexity?
A. O(n^2)
B. O(n)
C. O(log n)
D. O(n^3)
CORRECT ANSWER: D. O(n^3)
Rationale: O(n^3) is the standard Big O notation that represents cubic time complexity,
indicating how the runtime scales with input size.
Question 15: Which Big O notation best describes exponential time complexity?
A. O(n!)
B. O(n)
C. O(2^n)
D. O(n^2)
CORRECT ANSWER: C. O(2^n)
Rationale: O(2^n) is the standard Big O notation that represents exponential time complexity,
indicating how the runtime scales with input size.
Question 16: Which Big O notation best describes factorial time complexity?

, A. O(2^n)
B. O(n!)
C. O(n)
D. O(log n)
CORRECT ANSWER: B. O(n!)
Rationale: O(n!) is the standard Big O notation that represents factorial time complexity,
indicating how the runtime scales with input size.
Question 17: Which Big O notation best describes constant time complexity?
A. O(n^2)
B. O(n)
C. O(1)
D. O(log n)
CORRECT ANSWER: C. O(1)
Rationale: O(1) is the standard Big O notation that represents constant time complexity,
indicating how the runtime scales with input size.
Question 18: Which Big O notation best describes logarithmic time complexity?
A. O(n)
B. O(1)
C. O(n^2)
D. O(log n)
CORRECT ANSWER: D. O(log n)
Rationale: O(log n) is the standard Big O notation that represents logarithmic time complexity,
indicating how the runtime scales with input size.
Question 19: Which Big O notation best describes linear time complexity?
A. O(log n)
B. O(n^2)
C. O(1)
D. O(n)
CORRECT ANSWER: D. O(n)
Rationale: O(n) is the standard Big O notation that represents linear time complexity, indicating
how the runtime scales with input size.
Question 20: Which Big O notation best describes linearithmic time complexity?
A. O(n)
B. O(n log n)
C. O(1)
D. O(n^2)
CORRECT ANSWER: B. O(n log n)
Rationale: O(n log n) is the standard Big O notation that represents linearithmic time
complexity, indicating how the runtime scales with input size.
Question 21: What is the time complexity of accessing an element in an array by its index?
A. O(n)
B. O(log n)