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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Structures | 20% | - Primitive and composite data types - Data storage and retrieval principles - Arrays, linked lists, stacks, queues - Trees, graphs, hash tables |
| Topic 2: Algorithms & Complexity | 25% | - Big O notation, time and space complexity - Algorithm design and analysis - Recursion and iterative structures - Sorting and searching algorithms |
| Topic 3: Computer Architecture & Organization | 15% | - Instruction sets and execution cycles - Memory hierarchy and performance - CPU, memory, I/O systems - Von Neumann architecture |
| Topic 4: Discrete Mathematics & Logic | 25% | - Boolean algebra and digital logic - Set theory, relations, functions - Propositional and predicate logic - Proof techniques and mathematical induction |
| Topic 5: Software Engineering & Programming Basics | 15% | - Basic syntax and control structures - Testing and debugging fundamentals - Programming paradigms - Software development lifecycle |
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NEW QUESTION # 51
What is the first step in the selection sort algorithm?
Answer: A
Explanation:
Selection sort works by growing a sorted portion of the list one element at a time. The algorithm conceptually divides the array into two regions: asorted prefixon the left and anunsorted suffixon the right. At the beginning, the sorted prefix is empty and the entire list is unsorted. The first step is to consider position 0 as the target location for the smallest element. The algorithm scans the unsorted region (initially the whole list) to find the smallest valueand records its index. That action is exactly what option C describes: determine the lowest value starting from the first position.
After identifying the minimum element, selection sort swaps it into position 0 (if it isn't already there). Then it repeats the process for position 1, scanning the remaining unsorted suffix to find the next smallest element, swapping it into place, and so on. Textbooks emphasize that the key characteristic of selection sort is the repeated "select min (or max) from unsorted region and place it into the sorted region." Option A is not the standard first step; finding both min and max is unnecessary. Option B describes an unrelated swap that doesn't ensure progress toward sorting. Option D is not a "first step" but rather a different ordering goal; selection sort can be adapted for descending order, but the canonical version begins by selecting the minimum for the first position.
NEW QUESTION # 52
How can someone subset the last two rows and columns of a 2D NumPy array?
Answer: B
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 # 53
What Python code would return the value 40 from np_2d, where np_2d = np.array([[1, 2, 3, 4], [10, 20, 30,
40]])?
Answer: C
Explanation:
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.
NEW QUESTION # 54
What is a key advantage of using NumPy when handling large datasets?
Answer: C
Explanation:
NumPy's key advantage for large datasets isefficient storage and fast computation. Unlike Python lists, which store references to objects and can have per-element overhead, NumPy arrays store data in a compact, homogeneous format (single dtype) in contiguous or strided memory. This reduces memory usage and improves cache locality, which is crucial for performance on large arrays. Additionally, NumPy operations are vectorized: many computations run in optimized compiled code rather than interpreted Python loops. This enables large speedups for arithmetic, linear algebra, statistics, and transformations over entire arrays.
Option A is incorrect because NumPy itself does not provide full machine learning algorithms; those are typically found in libraries like scikit-learn, though they build on NumPy. Option B is incorrect because NumPy does not automatically clean data; data cleaning is usually done with pandas or custom logic. Option D is incorrect because interactive visualizations are typically handled by libraries like matplotlib, seaborn, or plotly, not by NumPy.
Textbooks in scientific computing highlight that NumPy forms the computational foundation of the Python data ecosystem. Its array model supports broadcasting, slicing, and efficient aggregations, all of which are essential when working with millions of numeric values. By combining compact memory layout with compiled numerical kernels, NumPy enables scalable analysis and simulation workloads that would be slow or memory-heavy using pure Python lists.
NEW QUESTION # 55
What is the layer of programming between the operating system and the hardware that allows the operating system to interact with it in a more independent and generalized manner?
Answer: D
Explanation:
TheHardware Abstraction Layer (HAL)is a software layer that sits between the operating system kernel and the physical hardware. Its purpose is to hide hardware-specific details behind a consistent interface, allowing the OS to be more portable and easier to maintain across different hardware platforms. Textbooks explain that without abstraction, the OS would need extensive device- and architecture-specific code scattered throughout the kernel, making updates and cross-platform support far more difficult.
The HAL typically provides standardized functions for interacting with low-level components such as interrupts, timers, memory mapping, and device I/O. With a HAL, the OS can call general routines (for example, to configure an interrupt controller) while the HAL handles the platform-specific implementation.
This supports a key systems principle: separate policy (what the OS wants to do) from mechanism (how hardware accomplishes it).
The other options are not correct. A boot loader runs at startup to load the operating system into memory; it is not the general interface layer during normal operation. The task scheduler is a kernel subsystem that manages CPU time among processes, not a hardware-independence layer. The file system layer manages storage organization and access semantics; it is not the general abstraction for all hardware interactions.
Therefore, the programming layer that enables generalized OS interaction with hardware is the hardware abstraction layer.
NEW QUESTION # 56
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