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| Section | Objectives |
|---|---|
| Data & Security Basics | - Data Handling
|
| Operating Systems & Architecture | - System Architecture
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| Programming Foundations | - Language Concepts Overview
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| Computer Science Fundamentals | - Core CS Concepts
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질문 # 70
What is the alternative way to access the third element of the first row in np_2d?
정답：D
설명：
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.
질문 # 71
What Python code would return the value 2 from np_2d, where np_2d = np.array([[1, 2, 3, 4], [10, 20, 30,
40]])?
정답：C
설명：
NumPy arrays support multi-dimensional indexing using a comma-separated index tuple. For a 2D array, the first index selects the row and the second index selects the column. With np_2d = np.array([[1, 2, 3, 4], [10,
20, 30, 40]]), row 0 is [1, 2, 3, 4]. Within that row, column 1 is the second element, which is 2. Therefore, np_2d[0, 1] returns 2.
Option A is incorrect because np_2d[0,1] already produces a scalar (an integer), and indexing a scalar again with [1] is invalid. Option C, np_2d[2], attempts to access the third row, but this array has only two rows (indices 0 and 1), so it would raise an index error. Option D, np_2d[2, 0], also references a non-existent third row and would error.
This indexing rule is foundational in array-based computing: it provides direct access to elements without loops and supports efficient numerical computation. Understanding row/column indexing is essential for slicing, broadcasting, and matrix operations taught in scientific computing curricula.
질문 # 72
What is the main advantage of using NumPy arrays over regular Python lists for data analysis?
정답：B
설명：
The primary advantage of NumPy arrays in data analysis is their support for fast, vectorized computation over whole collections of numeric data. A NumPy `ndarray` stores elements in a contiguous memory block with a single, fixed data type, enabling efficient low-level operations implemented in optimized C/Fortran code. As a result, expressions like `arr + 5`, `arr * arr`, or `np.mean(arr)` operate over the entire array without explicit Python loops. This style is commonly called **vectorization**, and it is a central theme in scientific computing textbooks because it is both clearer to read and significantly faster for large datasets.
Option A describes a property of Python lists, not NumPy arrays. Python lists can mix types freely, but this flexibility comes with overhead. Option B is true-NumPy arrays typically hold a single dtype-but it is not the main advantage; it is more of an implementation feature that enables speed and memory efficiency.
Option D is not a defining advantage; both lists and arrays can be concatenated, and NumPy provides dedicated functions such as `np.concatenate`, but concatenation is not the core reason NumPy dominates data analysis workflows.
# Because NumPy operations are applied element-wise across entire arrays and can leverage CPU vector instructions and efficient memory access patterns, they form the foundation for higher-level tools like pandas, SciPy, and many machine learning libraries. This is why the best answer is that NumPy arrays can perform calculations over entire collections of values.
질문 # 73
What type of encryption is provided by encryption utilities built into the file system?
정답：A
설명：
File system encryption utilities are designed to protect datastored on a disk-for example, files on an SSD, HDD, or other persistent storage. This protection is calledencryption at rest. The key idea is that if an attacker steals the physical drive, gains access to a powered-off machine, or otherwise reads storage directly, the raw bytes on disk remain unreadable without the correct cryptographic key. Common textbook examples include full-disk encryption and per-file encryption supported by operating systems and file systems.
This differs fromencryption in motion(also called encryption in transit), which protects data while it is being transmitted over networks, such as via TLS/HTTPS, VPNs, or secure messaging protocols. File system utilities do not primarily address network transmission; they address stored data confidentiality. Option B,
"encryption authentication," is not a standard category; authentication is a security goal often achieved using mechanisms like digital signatures, MACs, certificates, and protocol handshakes, not a type of file system encryption. Option D, steganography, is the practice of hiding information within other data (like images or audio) rather than encrypting it for confidentiality.
In short, file system encryption utilities aim to ensure that stored files remain confidential if storage is accessed without authorization, which is precisely the definition of encryption at rest.
질문 # 74
The np_2d array stores information about multiple family members. Each row represents a different person, and the columns store family member attributes in the following order:
Age (years)
Weight (pounds)
Height (inches)
How is the weight of all family members selected from the np_2d array?
정답：B
설명：
In a 2D NumPy array, rows and columns represent different dimensions of the data. The indexing form array
[row_selection, column_selection] allows you to select entire rows, entire columns, or submatrices. The slice :
means "all indices along this dimension." Since each row corresponds to a family member (a person), selecting weights forallfamily members means selectingall rowsfor the weight column.
The problem states the columns are ordered as: Age (column 0), Weight (column 1), Height (column 2).
Therefore, the weight column has index 1. The expression np_2d[:, 1] uses : to take every row and 1 to take the second column, producing a 1D array (or a column view) containing the weight values for all people.
Option A, np_2d[:, 2], would select the height column, not weight. Option C, np_2d[2, :], selects the third row (the third person) and all columns-age, weight, and height for just that one person. Option D, np_2d[1, :], selects the second person's entire row.
This column selection technique is fundamental in data analysis because datasets are often stored as
"rows = observations, columns = features," and extracting a feature vector is a frequent operation before computing statistics or building models.
질문 # 75
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