WGU C949 Data Structures and Algorithms I – Objective
Assessment Exam 2025 | Real Questions &
Correct Detailed Answers [Expert Verified] |
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✅ Includes the important keywords (WGU C949, Data
Structures and Algorithms I, Objective Assessment,
Exam, Questions and Answers, Verified)
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and exam-related
A functions whose cost scales linearly with the size of the input
O(n)
Iterating over a collection of data once often indicates an algorithm. (alphabet for-loop example)
O(n)
A functions whose cost scales logarithmically with the input size
O(log n)
Which type of function works by breaking down large problem into smaller and smaller chunks?
O(log n)
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)
A function that exhibits quadratic growth relative to the input size
,O(n^2)
, An example of this type of function is doubly nested loop
O(n^2)
Which type of function gets really expensive really quickly?
O(n^2)
A function that has two inputs that contribute to growth
O(nm)
An example of this type of function is when there is a nested loop that iterates of two distinct
collections of data
O(nm)
Are Big-O cases used in the best or worst situations?
Worst
Which statement is static?
readonly Contact[] contacts = new Contact[];
readonly Contact contacts = new Contacts[100];
readonly Contact contacts = new Contacts[100];
A container where data is stored in nodes consisting of a single data item and a reference to the
next node
Assessment Exam 2025 | Real Questions &
Correct Detailed Answers [Expert Verified] |
Guaranteed A Grade Success
✅ Includes the important keywords (WGU C949, Data
Structures and Algorithms I, Objective Assessment,
Exam, Questions and Answers, Verified)
✅ Optimized for visibility while keeping it professional
and exam-related
A functions whose cost scales linearly with the size of the input
O(n)
Iterating over a collection of data once often indicates an algorithm. (alphabet for-loop example)
O(n)
A functions whose cost scales logarithmically with the input size
O(log n)
Which type of function works by breaking down large problem into smaller and smaller chunks?
O(log n)
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)
A function that exhibits quadratic growth relative to the input size
,O(n^2)
, An example of this type of function is doubly nested loop
O(n^2)
Which type of function gets really expensive really quickly?
O(n^2)
A function that has two inputs that contribute to growth
O(nm)
An example of this type of function is when there is a nested loop that iterates of two distinct
collections of data
O(nm)
Are Big-O cases used in the best or worst situations?
Worst
Which statement is static?
readonly Contact[] contacts = new Contact[];
readonly Contact contacts = new Contacts[100];
readonly Contact contacts = new Contacts[100];
A container where data is stored in nodes consisting of a single data item and a reference to the
next node
