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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Discrete Mathematics & Logic | 25% | - Proof techniques and mathematical induction - Set theory, relations, functions - Propositional and predicate logic - Boolean algebra and digital logic |
| Topic 2: Computer Architecture & Organization | 15% | - Instruction sets and execution cycles - Von Neumann architecture - CPU, memory, I/O systems - Memory hierarchy and performance |
| Topic 3: Algorithms & Complexity | 25% | - Algorithm design and analysis - Big O notation, time and space complexity - Sorting and searching algorithms - Recursion and iterative structures |
| Topic 4: Data Structures | 20% | - Primitive and composite data types - Arrays, linked lists, stacks, queues - Trees, graphs, hash tables - Data storage and retrieval principles |
| Topic 5: Software Engineering & Programming Basics | 15% | - Testing and debugging fundamentals - Programming paradigms - Software development lifecycle - Basic syntax and control structures |
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NEW QUESTION # 50
Which method converts the default smallest-to-largest index order of a list to instead be the opposite?
Answer: A
Explanation:
Python lists maintain an order, and sometimes you need to reverse that order so the last element becomes first and the first becomes last. The standard list method for reversing the elementsin placeis reverse(). For example, if nums = [1, 2, 3, 4], then nums.reverse() mutates the list so it becomes [4, 3, 2, 1]. This is a built-in operation taught in introductory programming texts because it is efficient and conceptually simple: it does not create a new list unless you explicitly copy the data.
It is important to distinguish reversing from sorting. Reversing changes the sequence order as-is, while sorting rearranges elements according to comparisons. The question refers to converting the index order to the opposite, which is reversing. If you wanted descendingsortedorder, you would typically use sort (reverse=True) or sorted(nums, reverse=True). But the direct method that reverses the list's order is reverse().
The other options are not standard Python list methods. sortDescending(), flip(), and invert() are not part of Python's built-in list API. Textbooks emphasize learning the correct method names because Python's standard library provides a consistent, widely used interface across programs. Thus, reverse() is the correct answer for reversing the index order of a list.
NEW QUESTION # 51
Which line of code below contains an error in the use of NumPy?
Answer: C
Explanation:
The NumPy library provides arrays and efficient numerical operations, including sorting. However, NumPy doesnotprovide a function named np.quicksort. That is the API misuse in the code, making option A the correct answer. In NumPy, sorting is commonly performed using np.sort(arr) (which returns a sorted copy) or arr.sort() (which sorts in-place). If a specific algorithm is desired, NumPy exposes it through the kind parameter, such as np.sort(arr, kind="quicksort"), kind="mergesort", or kind="heapsort". Textbooks present this as a typical design: a single sorting interface with selectable strategies, rather than separate top-level functions per algorithm name.
Option C is correct and necessary: import numpy as np is standard convention. Option B is also correct:
printing a variable is valid assuming it exists. Option D, written as arr = np.array([3, 2, 0, 1]), is valid NumPy usage for constructing a 1D array from a Python list.
A subtle point taught in scientific computing courses is that library APIs matter as much as syntax: you can write perfectly valid Python that still fails if you call a function that the library does not define. In this case, the fix is to replace np.quicksort(arr) with np.sort(arr) or np.sort(arr, kind="quicksort") depending on whether you need to specify the algorithm.
NEW QUESTION # 52
What happens if one element of a NumPy array is changed to a string?
Answer: C
Explanation:
A central rule in NumPy is that an ndarray has a single, fixed data type called itsdtype. That dtype is chosen when the array is created (for example, int64, float64, etc.), and it normally does not change just because you assign a new value into one element. When you attempt an assignment, NumPy tries tocastthe assigned value into the array's existing dtype. If the cast is possible, the assignment succeeds; if the cast is impossible, NumPy raises an error.
So, if you have a numeric array such as arr = np.array([1, 2, 3]), its dtype is an integer type. Trying arr[0] =
"hello" cannot be converted into an integer, so NumPy raises a ValueError (a casting/conversion error). This is exactly the behavior textbooks highlight when contrasting NumPy arrays with Python lists: lists can hold mixed types freely, but NumPy arrays trade that flexibility for speed and memory efficiency via uniform typing.
Option A is a common misconception. While NumPy may "upcast" values to a more general dtype at array creation time when mixed types are provided (e.g., numbers and strings in the same constructor), a pre-existing numeric array will not automatically convert itself into a string array during a single- element assignment. Options C and D do not reflect NumPy's assignment rules.
NEW QUESTION # 53
How is the NumPy package imported into a Python session?
Answer: C
Explanation:
In Python, external libraries are brought into a program using the import statement. NumPy, which provides the ndarray type and a large collection of numerical computing functions, is conventionally imported with an alias for convenience. The standard and widely taught pattern is import numpy as np. This imports the numpy module and binds it to the shorter name np, making code more readable and reducing repeated typing, especially in mathematical expressions such as np.array(...), np.mean(...), or np.dot(...).
Option A is incorrect because the module name is numpy, not num_py. Options C and D resemble syntax from other languages (for example, "using" in C# or "include" in C/C++), but they are not valid Python import mechanisms. Python's module system is based on imports, and the aliasing feature (as np) is built into the import statement.
Textbooks also emphasize that importing a package requires that it be installed in the active Python environment. If NumPy is not installed, import numpy as np will raise an ImportError (or ModuleNotFoundError in modern Python). Once imported, the alias np is used consistently in scientific computing materials, notebooks, and professional data analysis codebases, which is why this option is considered the correct and expected answer.
NEW QUESTION # 54
What happens if you try to create a NumPy array with different types?
Answer: A
Explanation:
When NumPy constructs an ndarray, it chooses a single data type called the dtype for the entire array. This is a defining feature of NumPy arrays: unlike Python lists, which can hold mixed object types freely, a NumPy array is designed for efficient numerical computation by storing values in a uniform, contiguous representation. Therefore, if you provide mixed types at creation time, NumPy will select a dtype that can represent all provided values and will convert elements as needed.
This process is commonly described as type promotion or coercion to a common type. For example, mixing integers and floats produces a float array because floats can represent integers without loss of generality.
Mixing numbers and strings often results in a string dtype (or, in some cases, an object dtype), because numbers can be converted to their string representations. Once the dtype is chosen, the array behaves consistently under vectorized operations appropriate for that dtype.
Option B correctly summarizes this textbook behavior: the array will contain a single type, converting all elements to that type. Option A is too absolute-many mixed-type arrays still support calculations depending on the resulting dtype. Option C is vague and misses the crucial fact that conversion occurs. Option D is not how NumPy works; it never automatically splits inputs into multiple arrays by type.
Understanding dtype coercion matters because it affects memory usage, performance, and whether numerical operations behave as expected.
NEW QUESTION # 55
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