WGU Certification Exams Pack
Everything from Basic, plus:
- Exam Name: WGU Foundations of Computer Science
- 70 Questions Answers with Explanation Detail
- Total Questions: 70 Q&A's
- Single Choice Questions: 70 Q&A's
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WGU Foundations-of-Computer-Science - WGU Foundations of Computer Science
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Total Questions: 70
Free Practice Questions: 21
Which Python command can be used to display the results of calculations?
Answer
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A
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Explanation
In Python, the standard way to display output to the console is the built-in function print(). When a program performs calculations—such as arithmetic expressions, function results, or computed statistics—print() can be used to show those results to the user. For example, print(2 + 3) displays 5, and print(total / count) displays the computed average. Textbooks introduce print() early because it supports interactive learning, debugging, and communicating program behavior. print() can display one or multiple items separated by commas, automatically converting them to string form. It also supports formatting via f-strings (e.g., print(f"Sum = {s}")) and optional parameters like sep and end to control output formatting. This makes it versatile for reporting calculated values, intermediate steps in algorithms, and final program outputs. The other options are not standard Python built-ins for output. compute(), result(), and solve() are not universally defined commands in Python; they might exist as user-defined functions or in specific libraries, but they are not the general command taught in textbooks for displaying results. Python follows a clear separation: expressions compute values; print() displays them. Therefore, the correct answer is print(), as it is the primary mechanism for producing human-readable output from calculations in typical Python programs and coursework. |
How is a NumPy array named data with 6 elements reshaped into a 2x3 array?
Answer
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A
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Explanation
Reshaping is the operation of changing the “view” of an array so that the same elements are arranged with new dimensions. In NumPy, reshaping is possible when the total number of elements stays the same. A 2x3 array contains 6 elements, so a 1D array data of length 6 can be reshaped into shape (2, 3) without adding or removing values. Textbooks stress this invariant: the product of the dimensions must equal the original size. NumPy provides two standard reshaping interfaces: the function np.reshape(data, (2, 3)) and the method data.reshape(2, 3) (or data.reshape((2, 3))). Option A is correct because it uses the official NumPy function with the proper arguments: the original array and the target shape. The shape is passed as a tuple describing rows and columns. Option B is incorrect because np_reshape is not the correct NumPy function name, and it references an unrelated identifier list. Option C is incorrect because NumPy arrays do not provide a set_shape method like that. Option D is not valid NumPy syntax for reshaping. Reshaping is fundamental in data analysis and machine learning: it converts flat vectors into matrices, prepares batches of samples, and aligns dimensions for matrix multiplication and broadcasting. |
What is the alternative way to access the third element of the first row in np_2d?
Answer
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D
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Explanation
NumPy arrays use zero-based indexing, meaning counting starts at 0 rather than 1. In a 2D NumPy array, indexing is typically written in the form array[row_index, column_index]. The first index selects the row, and the second index selects the column. Therefore, the “first row” corresponds to row index 0. Within that row, the “third element” corresponds to column index 2, because the columns are indexed 0, 1, 2, 3, and so on. So, np_2d[0, 2] directly selects the element at row 0 and column 2, which is the third element in the first row. This is considered an “alternative” to approaches like two-step indexing (np_2d[0][2]), and it is the standard idiom taught for multi-dimensional NumPy arrays. The other choices point to different locations. np_2d[1, 3] is the fourth element of the second row, not the third element of the first row. np_2d[2, 0] and np_2d[3, 1] attempt to access the third or fourth row, which would often be out of bounds in a small 2-row example and would raise an IndexError. Correct indexing is a cornerstone of array programming because it determines which observation, feature, or matrix entry your computations will use. |
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WGU Foundations-of-Computer-Science FAQ'S
Find answers to the most common questions about the WGU Foundations-of-Computer-Science exam, including what it is, how to prepare, and how it can boost your career.
The WGU Foundations-of-Computer-Science certification is a globally-acknowledged credential that is awarded to candidates who pass this certification exam by obtaining the required passing score. This credential attests and validates the candidates' knowledge and hands-on skills in domains covered in the WGU Foundations-of-Computer-Science certification syllabus. The WGU Foundations-of-Computer-Science certified professionals with their verified proficiency and expertise are trusted and welcomed by hiring managers all over the world to perform leading roles in organizations. The success in WGU Foundations-of-Computer-Science certification exam can be ensured only with a combination of clear knowledge on all exam domains and securing the required practical training. Like any other credential, WGU Foundations-of-Computer-Science certification may require periodic renewal to stay current with new innovations in the concerned domains.
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