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WGU Foundations-of-Computer-Science Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Algorithm Efficiency | - Choose an appropriate sorting algorithm method based on a given scenario - Describe the relationships between algorithm complexity and data structures - Choose an appropriate algorithm searching method based on a given scenario |
| OS Fundamentals | - Demonstrate various techniques and tools to manage operating systems - Identify common privacy and security concepts that could be implemented in operating systems - Describe fundamental principles and core concepts of operating systems |
| Data Profiling | - Utilize a programming language to manipulate arrays and discover insights - Apply fundamental concepts and subsetting techniques to a dataset |
| Basic Program Design | - Identify variables and data types within a programming language - Explain how to store, access, and manipulate data in lists - Use functions, methods, and packages to leverage programming language |
WGU Foundations of Computer Science Sample Questions:
1. What is the alternative way to access the third element of the first row in np_2d?
A) np_2d[1, 3]
B) np_2d[2, 0]
C) np_2d[3, 1]
D) np_2d[0, 2]
2. What is the expected result of running the following code: list1[0] = "California"?
A) The list will be extended by adding "California" at the end.
B) A second element will be added to the line "California".
C) The first value in the list will be replaced with "California".
D) A new list will be created with the value "California".
3. What stores the location of the next node in a linked list?
A) The value
B) The index
C) The pointer
D) The header
4. What happens if you try to create a NumPy array with different types?
A) The array will be created with no issues.
B) The array will contain a single type, converting all elements to that type.
C) The array will be split into multiple arrays, one for each type.
D) The array will be created, but calculations will not be possible.
5. How is a NumPy array named data with 6 elements reshaped into a 2x3 array?
A) np_reshape(list, (2, 3))
B) data_reshape[2, 3]
C) np.reshape(data, (2, 3))
D) data.set_shape(2, 3)
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: C |
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