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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Computer Architecture & Organization | 15% | - Von Neumann architecture - CPU, memory, I/O systems - Memory hierarchy and performance - Instruction sets and execution cycles |
| Topic 2: Software Engineering & Programming Basics | 15% | - Software development lifecycle - Testing and debugging fundamentals - Basic syntax and control structures - Programming paradigms |
| Topic 3: Algorithms & Complexity | 25% | - Sorting and searching algorithms - Algorithm design and analysis - Recursion and iterative structures - Big O notation, time and space complexity |
| Topic 4: Data Structures | 20% | - Trees, graphs, hash tables - Data storage and retrieval principles - Primitive and composite data types - Arrays, linked lists, stacks, queues |
| 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 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 # 68
What is the output of print(employees[3]) when employees = ["Anika", "Omar", "Li", "Alex"]?
Answer: B
Explanation:
Python lists are ordered sequences indexed starting from 0. This zero-based indexing is standard in many programming languages and is a core concept in data structures. For the list `employees = ["Anika", "Omar",
"Li", "Alex"]`, the mapping of indices to elements is: index 0 # "Anika", index 1 # "Omar", index 2 # "Li", index 3 # "Alex". Therefore, the expression `employees[3]` selects the element at index 3, which is `"Alex"`, and `print(employees[3])` outputs `Alex` (strings print without quotes in normal output).
Option A would be correct for `employees[1]`, option D would be correct for `employees[2]`, and option C would be correct for `employees[0]`. This kind of question tests understanding of list indexing, which is essential for iteration, slicing, and algorithm implementation.
# Textbooks also note the difference between indexing and slicing: indexing returns a single element, while slicing returns a sublist. Here, because square brackets contain a single integer index, it is indexing. If you attempted an index that is out of range, Python would raise an `IndexError`, which reinforces careful reasoning about list length and positions. Understanding these fundamentals is critical for correctly manipulating datasets, where row/column positions and offsets frequently matter.
NEW QUESTION # 69
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 # 70
What is the time complexity of a binary search algorithm?
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