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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Discrete Mathematics & Logic | 25% | - Propositional and predicate logic - Boolean algebra and digital logic - Proof techniques and mathematical induction - Set theory, relations, functions |
| Topic 2: Algorithms & Complexity | 25% | - Big O notation, time and space complexity - Sorting and searching algorithms - Algorithm design and analysis - Recursion and iterative structures |
| Topic 3: Data Structures | 20% | - Trees, graphs, hash tables - Primitive and composite data types - Data storage and retrieval principles - Arrays, linked lists, stacks, queues |
| Topic 4: Software Engineering & Programming Basics | 15% | - Testing and debugging fundamentals - Software development lifecycle - Programming paradigms - Basic syntax and control structures |
| Topic 5: Computer Architecture & Organization | 15% | - Von Neumann architecture - Instruction sets and execution cycles - CPU, memory, I/O systems - Memory hierarchy and performance |
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NEW QUESTION # 57
Which sorting algorithm works by finding the smallest or largest element in an unsorted part of a list and moving it to the sorted part of the list?
Answer: A
Explanation:
Selection sort is defined by a simple repeated strategy: divide the list into a sorted region and an unsorted region, then repeatedly select the smallest (or largest) element from the unsorted region and move it to the end of the sorted region. In the common "smallest-first" version, the algorithm scans the unsorted portion to find the minimum element, then swaps it into the next position in the sorted portion. After the first pass, the smallest element is fixed at index 0; after the second pass, the second-smallest is fixed at index 1; and so on until the entire list is sorted.
This exactly matches the description in the question, making selection sort the correct answer. Textbooks often use selection sort to teach algorithmic thinking because it is easy to understand and implement, though not efficient for large datasets. Its time complexity is O(n²) in the average and worst case because it performs roughly n scans of progressively smaller unsorted sections, with each scan taking linear time. Its space usage is O(1) additional space because it sorts in place using swaps.
The other options do not match the described mechanism. Quicksort partitions around a pivot, heap sort uses a heap data structure to repeatedly extract the maximum/minimum, and radix sort processes digits/keys by place value rather than selecting minima by scanning. Selection sort's defining action is the repeated "select the min/max and place it."
NEW QUESTION # 58
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 # 59
What will be the result of performing the slice fam[:3]?
Answer: A
Explanation:
Python slicing uses the notation sequence[start:stop], where start is inclusive and stop is exclusive. When start is omitted, it defaults to 0, meaning the slice starts from the beginning of the sequence. Therefore, fam[:3] is equivalent to fam[0:3]. Because the stop index 3 is excluded, the slice includes elements at indices 0, 1, and
2-exactly the first three elements.
This convention is emphasized in programming textbooks because it makes many tasks natural and reduces boundary errors. For example, "take the first n items" is written as [:n], and "drop the first n items" is written as [n:]. The length of the slice is also easy to reason about: with step 1, it is stop - start, so here it is 3 - 0 = 3.
Option B is incorrect because including four elements would require fam[:4]. Option C would correspond to fam[:2]. Option D describes taking elements from the end, which would use negative indexing such as fam
[-3:].
Slicing is widely used for batching, windowing in algorithms, splitting datasets into training/testing segments, and extracting prefixes in parsing tasks. Understanding the inclusive start and exclusive stop rule is essential for correct Python programming.
NEW QUESTION # 60
How does the data type of a variable get set in Python?
Answer: A
Explanation:
Python usesdynamic typing, a core concept emphasized in programming language textbooks. In dynamically typed languages, a variable name does not permanently "own" a type. Instead, theobjectcreated by an expression has a type, and the variable becomes a reference to that object. Therefore, the type associated with a variable at any moment is determined by the value assigned to it. For example, after x = 7, x refers to an integer object. After x = "seven", the same name now refers to a string object. The type changes because the binding changes, not because the variable's type declaration was edited.
Option A describesstatic typingsystems (common in languages like Java, C, or C++), where programmers declare types and compilers enforce them. Python does not require such declarations for ordinary variables.
Option B is incorrect because type assignment is deterministic, not random. Option C is incorrect because Python does not default variables to strings; it assigns whatever type results from the right-hand-side expression.
This model is closely tied to Python's runtime behavior: type checks occur during execution, and functions can accept values of different types as long as the operations used are valid (often discussed as
"duck typing"). This flexibility supports rapid development, but also motivates careful testing and, in larger systems, optional type hints for documentation and tool support.
NEW QUESTION # 61
Which statement describes the data type restriction found in most NumPy arrays?
Answer: D
Explanation:
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.
NEW QUESTION # 62
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