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| Section | Objectives |
|---|---|
| Topic 1: Data & Security Basics | - Security Fundamentals
|
| Topic 2: Programming Foundations | - Programming Concepts
|
| Topic 3: Computer Science Fundamentals | - Data Structures Introduction
|
| Topic 4: Operating Systems & Architecture | - OS Fundamentals
|
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NEW QUESTION # 51
Which aspect of a security policy would define the ramifications of abusing company resources?
Answer: A
Explanation:
AnAcceptable Use Policy (AUP)defines how employees and users are permitted to use an organization's computing resources-such as email, internet access, file storage, endpoints, and networks-and it typically specifies prohibited behaviors and the consequences of violations. In security and IT governance textbooks, the AUP is framed as both a behavioral contract and a risk-management tool: it reduces misuse, clarifies expectations, and provides an enforceable basis for disciplinary action.
The "ramifications of abusing company resources" (for example, installing unauthorized software, excessive personal use, accessing inappropriate content, attempting to bypass security controls, or sharing credentials) are precisely the kinds of issues an AUP addresses. The policy often includes monitoring statements (users have limited expectation of privacy), compliance requirements, and escalation paths for violations.
A Network Security Policy (A) focuses on technical rules for network protection-firewalls, segmentation, remote access, and intrusion detection-rather than broad user conduct and disciplinary consequences. A Physical Security Policy (B) addresses protection of facilities and hardware-badges, locks, visitor procedures, secure areas. A Data Retention Policy (D) defines how long data is stored, how it is archived, and how it is disposed, which is different from defining misuse consequences.
Thus, the policy aspect that defines permissible behavior and the consequences for abusing resources is the Acceptable Use Policy.
NEW QUESTION # 52
What type of encryption is provided by encryption utilities built into the file system?
Answer: B
Explanation:
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.
NEW QUESTION # 53
What is an ndarray in Python?
Answer: B
Explanation:
An ndarray is NumPy's fundamental data structure: ann-dimensional arraydesigned for efficient numerical computation. The term stands for "N-dimensional array," and it is implemented as numpy.ndarray. Unlike Python's built-in list, an ndarray stores elements in a compact, homogeneous format defined by its dtype (such as integers or floating-point numbers). This uniform representation enables fast, vectorized operations and efficient use of memory, which is why ndarray is central in scientific computing and data analysis.
An ndarray supports multiple dimensions: a 1D array behaves like a vector, a 2D array like a matrix (rows and columns), and higher-dimensional arrays represent tensors. Textbooks emphasize that ndarray operations are typically element-wise by default (for example, a + b adds corresponding elements), and that slicing and broadcasting allow powerful computations without explicit loops. This approach is both expressive and efficient because the heavy lifting happens in optimized low-level code.
Option A is incorrect because ndarray is not built into core Python; it comes from NumPy. Option B describes a tree, which is a different data structure entirely. Option D is incorrect because sockets and XML-related functionality belong to other parts of Python's standard library, not to NumPy or ndarray.
In short, an ndarray is the primary array object of NumPy, providing high-performance multi- dimensional numerical storage and computation.
NEW QUESTION # 54
How can someone subset the last two rows and columns of a 2D NumPy array?
Answer: D
Explanation:
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].
NEW QUESTION # 55
Which method converts the default smallest-to-largest index order of a list to instead be the opposite?
Answer: C
Explanation:
Python lists maintain an order, and sometimes you need to reverse that order so the last element becomes first and the first becomes last. The standard list method for reversing the elementsin placeis reverse(). For example, if nums = [1, 2, 3, 4], then nums.reverse() mutates the list so it becomes [4, 3, 2, 1]. This is a built-in operation taught in introductory programming texts because it is efficient and conceptually simple: it does not create a new list unless you explicitly copy the data.
It is important to distinguish reversing from sorting. Reversing changes the sequence order as-is, while sorting rearranges elements according to comparisons. The question refers to converting the index order to the opposite, which is reversing. If you wanted descendingsortedorder, you would typically use sort (reverse=True) or sorted(nums, reverse=True). But the direct method that reverses the list's order is reverse().
The other options are not standard Python list methods. sortDescending(), flip(), and invert() are not part of Python's built-in list API. Textbooks emphasize learning the correct method names because Python's standard library provides a consistent, widely used interface across programs. Thus, reverse() is the correct answer for reversing the index order of a list.
NEW QUESTION # 56
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