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  • Exam Code: Foundations-of-Computer-Science
  • Exam Name: WGU Foundations of Computer Science
  • Last Updated: Aug 24, 2026
  • Q & A: 72 Questions and Answers
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  • Exam Code: Foundations-of-Computer-Science
  • Exam Name: WGU Foundations of Computer Science
  • Last Updated: Aug 24, 2026
  • Q & A: 72 Questions and Answers
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WGU Foundations-of-Computer-Science Exam Syllabus Topics:

SectionObjectives
OS Fundamentals- Describe fundamental principles and core concepts of operating systems
- Identify common privacy and security concepts that could be implemented in operating systems
- Demonstrate various techniques and tools to manage operating systems
Data Profiling- Apply fundamental concepts and subsetting techniques to a dataset
- Utilize a programming language to manipulate arrays and discover insights
Basic Program Design- Explain how to store, access, and manipulate data in lists
- Use functions, methods, and packages to leverage programming language
- Identify variables and data types within a programming language
Algorithm Efficiency- Choose an appropriate algorithm searching method based on a given scenario
- Describe the relationships between algorithm complexity and data structures
- Choose an appropriate sorting algorithm method based on a given scenario

WGU Foundations of Computer Science Sample Questions:

1. What is the output of print(employees[3]) when employees = ["Anika", "Omar", "Li", "Alex"]?

A) "Li"
B) "Alex"
C) "Anika"
D) "Omar"


2. What is another term for the inputs into a function?

A) Variables
B) Arguments
C) Outputs
D) Procedures


3. Which Python function is used to display the data type of a given variable?

A) Show()
B) Data()
C) type()
D) GetVar()


4. Which statement describes the data type restriction found in most NumPy arrays?

A) NumPy arrays are restricted to string data types only.
B) NumPy arrays adapt to the most complex data type on the fly.
C) NumPy arrays can only hold integer data types.
D) NumPy arrays must be of the same type of data.


5. What is the main advantage of using NumPy arrays over regular Python lists for data analysis?

A) NumPy arrays can only hold elements of the same type.
B) NumPy arrays can concatenate lists by default.
C) NumPy arrays can bring different types into the array at the same time.
D) NumPy arrays can perform calculations over entire collections of values.


Solutions:

Question # 1
Answer: B
Question # 2
Answer: B
Question # 3
Answer: C
Question # 4
Answer: D
Question # 5
Answer: D

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