Foundations-of-Computer-Science題庫更新，Foundations-of-Computer-Science考試題庫

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我們的WGU Foundations-of-Computer-Science考古題資料是多功能的，簡單容易操作，亦兼容。通過使用我們上述題庫資料幫助你完成高品質的Foundations-of-Computer-Science認證，無論你擁有什么設備，我們題庫資料都支持安裝使用。最新的Foundations-of-Computer-Science考題資料不僅能幫助考生提高IT技能，還能保證你的利益，提供給你最好的服務，VCESoft將成為你一個值得信賴的伙伴。一年之內，你還享有更新你擁有題庫的權利，你就可以得到最新版的WGU Foundations-of-Computer-Science試題。

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>> Foundations-of-Computer-Science題庫更新 <<

Foundations-of-Computer-Science考試題庫，最新Foundations-of-Computer-Science題庫資源

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最新的 Courses and Certificates Foundations-of-Computer-Science 免費考試真題 (Q30-Q35):

問題 #30
What Python code would return the value 40 from np_2d, where np_2d = np.array([[1, 2, 3, 4], [10, 20, 30,
40]])?

答案：C

解題說明：
In a 2D NumPy array, indexing is written as array[row_index, column_index] using zero-based indices. The array np_2d = np.array([[1, 2, 3, 4], [10, 20, 30, 40]]) has two rows (indices 0 and 1) and four columns (indices 0, 1, 2, 3). The value 40 is located in the second row and the fourth column. Using zero-based indexing, that corresponds to row index 1 and column index 3. Therefore, np_2d[1, 3] returns 40.
Option A attempts to access row 3, which does not exist and would raise an IndexError. Option C attempts to access column 4 in row 0, but valid column indices are only 0 through 3, so it would also error. Option D likewise refers to a non-existent row 4. Only option B uses valid indices and points to the correct location.
Textbooks emphasize multi-dimensional indexing because it underlies matrix operations, dataset manipulation, and feature extraction in data science. Correctly interpreting rows and columns is essential when rows represent observations (like people) and columns represent attributes (like age, weight, height). This question tests precise control over row/column addressing, which prevents subtle bugs in numerical analysis.


問題 #31
Which file system is commonly used in Windows and supports file permissions?

答案：B

解題說明：
Windows commonly uses the NTFS (New Technology File System) for internal drives and many external drives because it supports advanced features required for modern operating systems. One of the most important features is support forfile and folder permissionsvia Access Control Lists (ACLs). Permissions enable the OS to enforce security policies by controlling which users and groups can read, write, execute, modify, or delete specific resources. This is fundamental to multi-user security and is a standard topic in operating systems and security textbooks.
FAT32 is an older file system designed for simplicity and broad compatibility. It does not provide the same fine-grained permission model as NTFS, which is why it is often used for removable media where cross- platform compatibility matters more than access control. HFS+ is historically associated with Apple's macOS systems, and EXT4 is widely used on Linux. While these file systems have their own permission and feature models, they are not the common Windows default for permission-managed storage in typical Windows deployments.
NTFS also supports journaling (improving reliability after crashes), large file sizes, quotas, compression, and encryption features (through Windows facilities). In enterprise environments, NTFS permissions integrate with Windows authentication and directory services, enabling centralized user management. Therefore, for Windows systems requiring file permissions, NTFS is the correct answer.


問題 #32
What is the expected output of calling .shape on a NumPy 2D array?

答案：C

解題說明：
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.


問題 #33
Which method allows a user to convert a string value to all capital letters in Python?

答案：A

解題說明：
In Python, strings are objects of type str, and the language provides many built-in string methods for common transformations. The standard method used to convert all alphabetic characters in a string to uppercase is upper(). For example, "Hello, World".upper() produces "HELLO, WORLD". This method is part of Python's core string API and is documented as returning anewstring because strings are immutable in Python; the original string is not modified.
Options A and D resemble methods from other programming languages. For instance, toUpperCase() is commonly seen in Java and JavaScript, not Python. Option B, makeUpper(), is not a standard method in Python's str type. Python's naming conventions for built-in methods are typically short and lowercase, which is consistent with upper(), lower(), strip(), and replace().
It is also important to note what upper() does and does not do. It affects letters according to Unicode case-mapping rules, so it works beyond ASCII and supports many languages. Non-alphabetic characters such as digits, punctuation, and whitespace remain unchanged. Because the method returns a new string, it supports functional-style programming and safe reuse of the original data. In many textbook examples, upper() is paired with input normalization tasks, such as case-insensitive comparisons and cleaning user-entered text.


問題 #34
What stores the location of the next node in a linked list?

答案：D

解題說明：
A linked list is a dynamic data structure made up of nodes, where each node typically contains two components: a data field (the value being stored) and a link field (commonly called a pointer or reference).
The pointer's role is to store the memory address (or reference) of the next node in the sequence, thereby maintaining the logical order of the list even though nodes may be scattered throughout memory. This is a key contrast with arrays, which store elements contiguously and rely on index arithmetic to locate the next element.
Because each node explicitly points to the next node, linked lists support efficient insertion and deletion operations compared with arrays. To insert a node, you allocate it and then adjust pointers so it fits into the chain. To delete a node, you redirect the pointer of the previous node to skip over the removed node.
Traversal is performed by starting at the head node and repeatedly following the pointer until a null reference indicates the end of the list.
The other options do not correctly describe what stores the location of the next node. An index is used in array-like structures, not in a standard linked list node. The value is the payload data, not the link.
The "header" (often called the head pointer) is an external reference to the first node, not the field inside each node that links to the next. Therefore, the correct answer is the pointer.


問題 #35
......

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