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| Section | Objectives |
|---|---|
| Topic 1: Programming Foundations | - Language Concepts Overview
|
| Topic 2: Computer Science Fundamentals | - Data Structures Introduction
|
| Topic 3: Data & Security Basics | - Data Handling
|
| Topic 4: Operating Systems & Architecture | - OS Fundamentals
|
>> WGU Foundations-of-Computer-Science Fragen Antworten <<
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49. Frage
What is the expected output of calling .shape on a NumPy 2D array?
Antwort: C
Begründung:
In NumPy, every ndarray has a shape attribute that describes the size of the array along each dimension. For a
2D array, shape returns a tuple with two integers: (number_of_rows, number_of_columns). For example, if a
= np.array([[1, 2, 3], [4, 5, 6]]), then a.shape is (2, 3), meaning 2 rows and 3 columns. This is a fundamental idea in matrix and array computing, because shape governs how indexing, slicing, broadcasting, and linear algebra operations behave.
Option A describes the dtype, which can be accessed with a.dtype, not a.shape. Option C is incorrect because shape provides per-dimension sizes, not their sum. Option D refers to the total number of elements, which NumPy provides via a.size (or equivalently np.prod(a.shape)).
Textbooks emphasize shape because many errors in numerical computing come from mismatched dimensions. For example, matrix multiplication requires compatible inner dimensions, and broadcasting rules depend on dimension sizes. By checking .shape, programmers can verify their data layout before applying algorithms, ensuring rows represent observations and columns represent features (or vice versa). Thus, for a 2D NumPy array, .shape indicates the number of rows and columns.
50. Frage
What is the alternative way to access the third element of the first row in np_2d?
Antwort: A
Begründung:
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.
51. Frage
Which character is used to indicate a range of values to be sliced into a new list?
Antwort: D
Begründung:
In Python, slicing is the standard mechanism for extracting arangeof elements from a sequence type such as a list, string, or tuple. The character that signals a slice range is thecolon:. The general slice syntax is sequence
[start:stop:step]. Most commonly, you see sequence[start:stop], where start is the index to begin from (inclusive) and stop is the index to end at (exclusive). This "inclusive start, exclusive stop" rule is emphasized in textbooks because it makes slice lengths easy to reason about: when step is 1, the number of elements returned is stop - start.
For example, if items = ["a", "b", "c", "d", "e"], then items[1:4] returns ["b", "c", "d"]. Omitting start defaults to the beginning (items[:3] gives the first three elements), and omitting stop defaults to the end (items[2:] gives everything from index 2 onward). The optional step supports patterns like items[::2] for every other element, and negative steps can reverse a sequence (items[::-1]).
The other characters do not define ranges in Python slicing: , separates items (or indices in multidimensional structures), + is addition/concatenation, and = is assignment. The colon is the slicing operator that indicates a range.
52. Frage
Which statement describes the data type restriction found in most NumPy arrays?
Antwort: D
Begründung:
Most NumPy arrays enforce a key constraint: all elements share the samedtype(data type). This uniform typing is foundational to NumPy's performance model. Because each element has the same size and representation, NumPy can store the array in a contiguous memory block and apply low-level, vectorized operations efficiently. This is why NumPy is widely used for numerical computing, statistics, and data analysis: operations like addition, multiplication, and reductions (sum/mean) can be implemented in optimized compiled code without per-element Python overhead.
Option B captures this textbook principle: elements in a typical ndarray are of the same data type. The other options are incorrect. NumPy is not restricted to strings (A), and it is not limited to integers (C); it supports floats, complex numbers, booleans, fixed-width strings, datetime types, and many others. Option D is misleading: NumPy does not continuously "adapt on the fly" during normal use. The dtype is generally fixed once the array exists. What NumPydoesdo is choose an appropriate common dtype when you create an array from mixed inputs (for example, mixing ints and floats yields floats). But after creation, assignments are cast into the existing dtype rather than dynamically changing the dtype to accommodate new values.
This restriction is precisely what differentiates NumPy arrays from Python lists and enables predictable memory layout and fast numerical computation.
53. Frage
Which sorting algorithm works by finding the smallest or largest element in an unsorted part of a list and moving it to the sorted part of the list?
Antwort: D
Begründung:
Selection sort is defined by a simple repeated strategy: divide the list into a sorted region and an unsorted region, then repeatedly select the smallest (or largest) element from the unsorted region and move it to the end of the sorted region. In the common "smallest-first" version, the algorithm scans the unsorted portion to find the minimum element, then swaps it into the next position in the sorted portion. After the first pass, the smallest element is fixed at index 0; after the second pass, the second-smallest is fixed at index 1; and so on until the entire list is sorted.
This exactly matches the description in the question, making selection sort the correct answer. Textbooks often use selection sort to teach algorithmic thinking because it is easy to understand and implement, though not efficient for large datasets. Its time complexity is O(n²) in the average and worst case because it performs roughly n scans of progressively smaller unsorted sections, with each scan taking linear time. Its space usage is O(1) additional space because it sorts in place using swaps.
The other options do not match the described mechanism. Quicksort partitions around a pivot, heap sort uses a heap data structure to repeatedly extract the maximum/minimum, and radix sort processes digits/keys by place value rather than selecting minima by scanning. Selection sort's defining action is the repeated "select the min/max and place it."
54. Frage
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