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| Section | Weight | Objectives |
|---|---|---|
| Data Structures | 20% | - Primitive and composite data types - Data storage and retrieval principles - Arrays, linked lists, stacks, queues - Trees, graphs, hash tables |
| Software Engineering & Programming Basics | 15% | - Programming paradigms - Basic syntax and control structures - Software development lifecycle - Testing and debugging fundamentals |
| Algorithms & Complexity | 25% | - Sorting and searching algorithms - Algorithm design and analysis - Recursion and iterative structures - Big O notation, time and space complexity |
| Computer Architecture & Organization | 15% | - CPU, memory, I/O systems - Von Neumann architecture - Memory hierarchy and performance - Instruction sets and execution cycles |
| Discrete Mathematics & Logic | 25% | - Set theory, relations, functions - Propositional and predicate logic - Boolean algebra and digital logic - Proof techniques and mathematical induction |
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NEW QUESTION # 36
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?
Answer: D
Explanation:
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.
NEW QUESTION # 37
What is the component of the operating system that manages core system resources but allows no user access?
Answer: A
Explanation:
Thekernelis the central component of an operating system responsible for managing core system resources. It controls CPU scheduling, memory management, process creation and termination, device I/O coordination, and system calls-the controlled interface through which user programs request services. In operating systems textbooks, the kernel is described as running in a privileged mode (often called kernel mode or supervisor mode), which restricts direct user access for security and stability. User programs typically run in user mode and cannot directly manipulate hardware or critical OS structures; instead, they must request operations via system calls, which the kernel validates and executes.
This separation prevents accidental or malicious actions from crashing the entire system or compromising other processes. For example, a user application cannot directly write to arbitrary memory addresses or reprogram devices; the kernel mediates access and enforces protection boundaries. This model is foundational to modern OS design and underpins features like virtual memory, access control, and multitasking.
File Explorer and the user interface layer are user-facing components that provide interaction and file browsing; they are not the privileged core resource manager. "Device driver manager" is not typically the name of a single OS component; while drivers and driver subsystems exist, they operate under kernel control and are part of the kernel or closely integrated with it.
Therefore, the OS component that manages core resources while disallowing direct user access is the kernel.
NEW QUESTION # 38
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 # 39
Which type of sorting algorithm starts at the first position and moves the pointer until the end of the list, determining the lowest value?
Answer: A
Explanation:
Selection sort is the algorithm that repeatedly scans the unsorted portion of a list to find the lowest (or highest) value and then places it into its correct position in the sorted portion. It begins at the first index (position 0) and treats that as the boundary between sorted and unsorted regions. On the first pass, it moves a scanning pointer through the entire list to determine the minimum element and swaps it into position 0. On the second pass, it starts from position 1, scans to the end to find the next minimum, and swaps it into position 1.
This continues until the list is sorted.
This matches the question's description: "starts at the first position and moves the pointer until the end of the list, determining the lowest value." Textbooks often describe selection sort with two indices: one for the current boundary position and one for scanning the remainder of the list to find the minimum. The algorithm is simple and uses O(1) extra space, but it is inefficient for large lists because it performs O(n²) comparisons regardless of input order.
The other options are not standard algorithm names in typical computer science curricula. While many sorting algorithms exist (insertion sort, merge sort, quicksort, heap sort), "incremental," "progressive," and "pointer sort" are not canonical textbook algorithms in this context. Therefore, the correct answer is selection sort.
NEW QUESTION # 40
How can someone subset the last two rows and columns of a 2D NumPy array?
Answer: C
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 # 41
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