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| Section | Objectives |
|---|---|
| Basic Program Design | - Use functions, methods, and packages to leverage programming language - Identify variables and data types within a programming language - Explain how to store, access, and manipulate data in lists |
| Data Profiling | - Utilize a programming language to manipulate arrays and discover insights - Apply fundamental concepts and subsetting techniques to a dataset |
| Algorithm Efficiency | - Describe the relationships between algorithm complexity and data structures - Choose an appropriate sorting algorithm method based on a given scenario - Choose an appropriate algorithm searching method based on a given scenario |
| 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 |
>> Foundations-of-Computer-Science완벽한 공부자료 <<
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질문 # 58
What Python code would return the value 40 from np_2d, where np_2d = np.array([[1, 2, 3, 4], [10, 20, 30,
40]])?
정답：C
설명：
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.
질문 # 59
What are Python functions that belong to specific Python objects?
정답：C
설명：
In object-oriented programming, amethodis a function that is associated with an object (or its class) and is called using the dot operator. In Python, everything is an object, and many operations are provided through methods. For example, "hello".upper() calls the upper method of a str object, and [1, 2, 3].append(4) calls the append method of a list object. Textbooks emphasize that methods operate on an object's internal state and typically receive the object itself as an implicit first argument (commonly named self in class definitions).
This is what distinguishes methods from standalone functions.
Modules, scripts, and libraries are different organizational concepts. Amoduleis a file containing Python code, including function and class definitions. Ascriptis a Python program intended to be run directly. A libraryis a collection of modules that provides reusable functionality. None of these terms specifically mean
"functions that belong to objects."
Understanding methods matters because it connects to encapsulation and abstraction: objects provide behaviors (methods) that manipulate their data in well-defined ways. This design enables clearer APIs and supports polymorphism, where different object types can expose methods with the same name but different implementations. In Python, method calls are central to working with built-in types (strings, lists, dictionaries) and with user-defined classes, making "methods" the correct term for functions that belong to specific objects.
질문 # 60
What stores the location of the next node in a linked list?
정답：D
설명：
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.
질문 # 61
Which Windows 11 tool enables a user to manually add a Bluetooth device if it does not automatically configure when first connected?
정답：B
설명：
When a Bluetooth device does not configure automatically, the underlying issue is often driver discovery, device enumeration, or the Bluetooth adapter's state. In Windows, the tool traditionally associated with manually managing hardware devices and their drivers isDevice Manager. It lets a user view hardware categories (including Bluetooth adapters), enable or disable devices, update drivers, uninstall and rescan, and address "unknown device" situations. These actions are core to manual configuration because they influence whether Windows can properly recognize and communicate with a Bluetooth device.
Windows 11 pairing itself is typically initiated from the Settings app under Bluetooth and devices, where a user chooses "Add device" to pair a new accessory. (Microsoft Support) However, among the options provided, only Device Manager is a hardware-configuration tool that can resolve situations where automatic configuration fails due to driver or adapter problems. Network-related tools do not handle local device drivers, Task Scheduler automates tasks rather than adding devices, and Windows Defender is focused on security and malware protection rather than device setup.
From a systems perspective, this reflects a key operating-systems concept: successful device use requires both discovery/pairing and a correctly installed driver stack. Device Manager is the standard interface for the driver and device side of that equation, which is why it is the best match to "manually add or configure" hardware in the given choices.
질문 # 62
What is the expected output of calling .shape on a NumPy 2D array?
정답：B
설명：
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.
질문 # 63
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