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| Section | Objectives |
|---|---|
| Topic 1: Programming Foundations | - Language Concepts Overview
|
| Topic 2: Data & Security Basics | - Security Fundamentals
|
| Topic 3: Operating Systems & Architecture | - OS Fundamentals
|
| Topic 4: Computer Science Fundamentals | - Data Structures Introduction
|
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질문 # 60
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 # 61
What is the expected output of numpy_array[1]?
정답：B
설명：
In Python and NumPy, indexing iszero-based, meaning the first element of a 1D sequence is at index 0, the second element is at index 1, and so on. A NumPy array behaves like a sequence for basic indexing, so numpy_array[1] returns the element stored at position 1 in the array. This is a fundamental concept taught in introductory programming and scientific computing: indexing selects a single element, while slicing selects a range.
For example, if numpy_array = np.array([5, 8, 13]), then numpy_array[0] is 5, numpy_array[1] is 8, and numpy_array[2] is 13. The expression numpy_array[1] therefore evaluates to thesecond element(8 in this example). This does not display the entire array (that would happen with print(numpy_array)), and it does not produce an error unless the array is too short. An error such as IndexError occurs only if index 1 is out of bounds, for example when the array has length 1 and you try to access numpy_array[1].
Textbooks emphasize careful reasoning about indices because off-by-one errors are common. In data analysis, correct indexing is crucial for extracting the right observations, features, or time steps from numerical datasets.
질문 # 62
Which statement describes the data type restriction found in most NumPy arrays?
정답：C
설명：
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.
질문 # 63
What stores the location of the next node in a linked list?
정답：B
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 # 64
How can someone subset the last two rows and columns of a 2D NumPy array?
정답：A
설명：
NumPy slicing uses the same start/stop rules as Python sequences, and it also supports negative indices to count from the end. In a 2D array, slicing is written as array[rows, columns]. To get thelast two rows, you use
-2: in the row position, meaning "start two rows from the end and go to the end." Similarly, to get thelast two columns, you use -2: in the column position. Combining these gives array[-2:, -2:], which selects the bottom- right 2×2 subarray.
Option A, array[-2:, :], selects the last two rows butall columns, so it is not restricted to the last two columns.
Option D, array[:, -2:], selects all rows but only the last two columns. Option B, array[-1:, -1:], selects only the last row and the last column, producing a 1×1 (or 1×1 view) subarray, not a 2×2.
This kind of slicing is widely taught because it is essential for matrix operations, extracting submatrices, working with sliding windows, and manipulating image or time-series data where "take the last k observations/features" is common. Negative indexing reduces errors and makes code clearer, especially compared with computing explicit indices like array[rows-2:rows, cols-2:cols].
질문 # 65
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