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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Structures | 20% | - Arrays, linked lists, stacks, queues - Data storage and retrieval principles - Primitive and composite data types - Trees, graphs, hash tables |
| Topic 2: Computer Architecture & Organization | 15% | - Von Neumann architecture - Instruction sets and execution cycles - Memory hierarchy and performance - CPU, memory, I/O systems |
| Topic 3: Software Engineering & Programming Basics | 15% | - Software development lifecycle - Testing and debugging fundamentals - Programming paradigms - Basic syntax and control structures |
| Topic 4: Algorithms & Complexity | 25% | - Big O notation, time and space complexity - Sorting and searching algorithms - Algorithm design and analysis - Recursion and iterative structures |
| Topic 5: Discrete Mathematics & Logic | 25% | - Propositional and predicate logic - Boolean algebra and digital logic - Set theory, relations, functions - Proof techniques and mathematical induction |
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NEW QUESTION # 67
What is the expected output of calling .shape on a NumPy 2D array?
Answer: A
Explanation:
In NumPy, every ndarray has a shape attribute that describes the size of the array along each dimension. For a
2D array, shape returns a tuple with two integers: (number_of_rows, number_of_columns). For example, if a
= np.array([[1, 2, 3], [4, 5, 6]]), then a.shape is (2, 3), meaning 2 rows and 3 columns. This is a fundamental idea in matrix and array computing, because shape governs how indexing, slicing, broadcasting, and linear algebra operations behave.
Option A describes the dtype, which can be accessed with a.dtype, not a.shape. Option C is incorrect because shape provides per-dimension sizes, not their sum. Option D refers to the total number of elements, which NumPy provides via a.size (or equivalently np.prod(a.shape)).
Textbooks emphasize shape because many errors in numerical computing come from mismatched dimensions. For example, matrix multiplication requires compatible inner dimensions, and broadcasting rules depend on dimension sizes. By checking .shape, programmers can verify their data layout before applying algorithms, ensuring rows represent observations and columns represent features (or vice versa). Thus, for a 2D NumPy array, .shape indicates the number of rows and columns.
NEW QUESTION # 68
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: D
Explanation:
Ahypervisoris the software layer that enables virtualization-running multiple operating systems concurrently on the same physical hardware as separate, isolated virtual machines (VMs). Operating systems textbooks describe the hypervisor as managing and multiplexing core hardware resources such as CPU, memory, storage, and I/O devices among multiple guest operating systems. Each VM behaves as if it has its own hardware, while the hypervisor enforces isolation and schedules resource usage.
Hypervisors come in two broad categories.Type 1 (bare-metal)hypervisors run directly on the hardware (common in data centers), whileType 2 (hosted)hypervisors run as applications on top of a host OS (common on desktops). In both cases, the hypervisor is the key tool that makes "more than one OS at a time" possible.
System Restore is a recovery feature, not a virtualization platform. A partition manager can split a disk into multiple partitions, which can support dual-boot setups, but that runs only one OS at a time, not concurrently as VMs. A bootloader selects which OS to start at boot time; again, that is not simultaneous virtualization. Therefore, the correct tool that allows running multiple operating systems simultaneously as virtual machines is the hypervisor.
NEW QUESTION # 69
Which Python function is used to display the data type of a given variable?
Answer: A
Explanation:
Python is a dynamically typed language, meaning variables do not require explicit type declarations; instead, objects carry type information at runtime. To inspect the type of an object, Python provides the built-in function type(). When you pass a variable or value into type(), it returns the object's class, which represents its data type. For example, type(5) returns <class 'int'>, type(3.14) returns <class 'float'>, and type("hello") returns <class 'str'>. This is commonly used in debugging, learning exercises, and when writing functions that must behave differently depending on input types.
Textbook discussions often pair type() with Python's object model: everything in Python is an object, and each object is an instance of some class. type() reveals that class. In addition, type() can be used in more advanced ways, such as dynamic class creation, but its foundational educational use is type inspection.
The other options are not correct because GetVar(), Show(), and Data() are not standard Python built- ins for type checking. While developers can define functions with those names, they are not part of Python's core language or standard library in the sense required by the question. For typical coursework and professional Python usage, the correct and universally accepted function is type().
NEW QUESTION # 70
m = 30
n = 30
What will be the output of print(id(m), id(n)) after executing the following code?
Answer: D
Explanation:
In Python, id(x) returns the "identity" of an object, which in CPython (the most common implementation) is typically the object's memory address. When you write m = 30 and n = 30, both names may refer to thesame integer objectbecause CPython caches a range of small integer objects for efficiency. This optimization means that commonly used small integers are pre-created and reused, so repeated occurrences of the same small integer literal often point to the same object, producing identical id() values. As a result, print(id(m), id (n)) will most likely displaytwo identical numbersin standard CPython builds when 30 falls within the cached range. (Real Python) This behavior is an implementation detail, but it is widely discussed in Python education because it illustrates the difference between object identity (whether two variables reference the same object) and value equality (whether two objects have the same value). Even if id(m) and id(n) were different in some edge environment, m == n would still be True because the values are equal; id() is about identity, not value. The options "0 0" and "Error" are not consistent with how id() works for valid objects.
NEW QUESTION # 71
How is a NumPy array named data with 6 elements reshaped into a 2x3 array?
Answer: C
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 # 72
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