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| Section | Weight | Objectives |
|---|---|---|
| Computer Architecture & Organization | 15% | - Memory hierarchy and performance - Von Neumann architecture - Instruction sets and execution cycles - CPU, memory, I/O systems |
| Software Engineering & Programming Basics | 15% | - Programming paradigms - Testing and debugging fundamentals - Software development lifecycle - Basic syntax and control structures |
| Discrete Mathematics & Logic | 25% | - Boolean algebra and digital logic - Set theory, relations, functions - Propositional and predicate logic - Proof techniques and mathematical induction |
| Algorithms & Complexity | 25% | - Recursion and iterative structures - Sorting and searching algorithms - Algorithm design and analysis - Big O notation, time and space complexity |
| Data Structures | 20% | - Primitive and composite data types - Trees, graphs, hash tables - Data storage and retrieval principles - Arrays, linked lists, stacks, queues |
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NEW QUESTION # 31
What is the name of the tool that can allow a device to run more than one operating system at a time as virtual machines?
Answer: A
NEW QUESTION # 32
Given the following code, what is the expected output?
Answer: B
Explanation:
In NumPy, a 2D array can be visualized as a table of rows and columns. When you write np_2d[0], you are usingzero-based indexingto select thefirst rowof that 2D array. This is a standard convention in Python and many other programming languages: index 0 refers to the first element, index 1 to the second, and so on.
Therefore, np_2d[0] returns all the elements in row 0.
With a typical construction such as np_2d = np.array([[1, 2, 3, 4], [10, 20, 30, 40]]), the first row is [1, 2, 3,
4], so printing np_2d[0] displays that row. NumPy returns the row as a 1D NumPy array, and when printed it often appears in bracket form like [1 2 3 4] (spaces rather than commas are common in NumPy's display).
Conceptually, however, the contents are exactly the first row values, matching option C.
Option A and D show the second row (index 1), not the first. Option B incorrectly suggests a column extraction rather than a row selection.
NEW QUESTION # 33
What are Python functions that belong to specific Python objects?
Answer: D
Explanation:
In object-oriented programming, amethodis a function that is associated with an object (or its class) and is called using the dot operator. In Python, everything is an object, and many operations are provided through methods. For example, "hello".upper() calls the upper method of a str object, and [1, 2, 3].append(4) calls the append method of a list object. Textbooks emphasize that methods operate on an object's internal state and typically receive the object itself as an implicit first argument (commonly named self in class definitions).
This is what distinguishes methods from standalone functions.
Modules, scripts, and libraries are different organizational concepts. Amoduleis a file containing Python code, including function and class definitions. Ascriptis a Python program intended to be run directly. A libraryis a collection of modules that provides reusable functionality. None of these terms specifically mean
"functions that belong to objects."
Understanding methods matters because it connects to encapsulation and abstraction: objects provide behaviors (methods) that manipulate their data in well-defined ways. This design enables clearer APIs and supports polymorphism, where different object types can expose methods with the same name but different implementations. In Python, method calls are central to working with built-in types (strings, lists, dictionaries) and with user-defined classes, making "methods" the correct term for functions that belong to specific objects.
NEW QUESTION # 34
How is a NumPy array named data with 6 elements reshaped into a 2x3 array?
Answer: A
Explanation:
Reshaping is the operation of changing the "view" of an array so that the same elements are arranged with new dimensions. In NumPy, reshaping is possible when the total number of elements stays the same. A 2x3 array contains 6 elements, so a 1D array data of length 6 can be reshaped into shape (2, 3) without adding or removing values. Textbooks stress this invariant: the product of the dimensions must equal the original size.
NumPy provides two standard reshaping interfaces: the function np.reshape(data, (2, 3)) and the method data.
reshape(2, 3) (or data.reshape((2, 3))). Option A is correct because it uses the official NumPy function with the proper arguments: the original array and the target shape. The shape is passed as a tuple describing rows and columns.
Option B is incorrect because np_reshape is not the correct NumPy function name, and it references an unrelated identifier list. Option C is incorrect because NumPy arrays do not provide a set_shape method like that. Option D is not valid NumPy syntax for reshaping.
Reshaping is fundamental in data analysis and machine learning: it converts flat vectors into matrices, prepares batches of samples, and aligns dimensions for matrix multiplication and broadcasting.
NEW QUESTION # 35
Which statement describes the data type restriction found in most NumPy arrays?
Answer: C
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 # 36
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