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| Section | Objectives |
|---|---|
| Data & Security Basics | - Data Handling
|
| Computer Science Fundamentals | - Data Structures Introduction
|
| Programming Foundations | - Language Concepts Overview
|
| Operating Systems & Architecture | - OS Fundamentals
|
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NEW QUESTION # 14
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: C
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 # 15
What is the expected output of numpy_array[1]?
Answer: C
Explanation:
In Python and NumPy, indexing iszero-based, meaning the first element of a 1D sequence is at index 0, the second element is at index 1, and so on. A NumPy array behaves like a sequence for basic indexing, so numpy_array[1] returns the element stored at position 1 in the array. This is a fundamental concept taught in introductory programming and scientific computing: indexing selects a single element, while slicing selects a range.
For example, if numpy_array = np.array([5, 8, 13]), then numpy_array[0] is 5, numpy_array[1] is 8, and numpy_array[2] is 13. The expression numpy_array[1] therefore evaluates to thesecond element(8 in this example). This does not display the entire array (that would happen with print(numpy_array)), and it does not produce an error unless the array is too short. An error such as IndexError occurs only if index 1 is out of bounds, for example when the array has length 1 and you try to access numpy_array[1].
Textbooks emphasize careful reasoning about indices because off-by-one errors are common. In data analysis, correct indexing is crucial for extracting the right observations, features, or time steps from numerical datasets.
NEW QUESTION # 16
What is the alternative way to access the third element of the first row in np_2d?
Answer: B
Explanation:
NumPy arrays use zero-based indexing, meaning counting starts at 0 rather than 1. In a 2D NumPy array, indexing is typically written in the form array[row_index, column_index]. The first index selects the row, and the second index selects the column. Therefore, the "first row" corresponds to row index 0. Within that row, the "third element" corresponds to column index 2, because the columns are indexed 0, 1, 2, 3, and so on.
So, np_2d[0, 2] directly selects the element at row 0 and column 2, which is the third element in the first row.
This is considered an "alternative" to approaches like two-step indexing (np_2d[0][2]), and it is the standard idiom taught for multi-dimensional NumPy arrays.
The other choices point to different locations. np_2d[1, 3] is the fourth element of the second row, not the third element of the first row. np_2d[2, 0] and np_2d[3, 1] attempt to access the third or fourth row, which would often be out of bounds in a small 2-row example and would raise an IndexError. Correct indexing is a cornerstone of array programming because it determines which observation, feature, or matrix entry your computations will use.
NEW QUESTION # 17
Which aspect is excluded from a NumPy array's structure?
Answer: C
Explanation:
A NumPy ndarray is designed for efficient numerical computing, and its structure is defined by metadata required to interpret a contiguous (or strided) block of memory as an n-dimensional array. Textbooks and NumPy's own conceptual model describe key components such as: adata buffer(where the raw bytes live), a data pointer(reference to the start of that buffer), thedtype(which specifies how to interpret each element's bytes-e.g., int32, float64), theshape(the size in each dimension), andstrides(how many bytes to step in memory to move along each dimension). Together, these allow fast indexing, slicing, and vectorized operations without Python-level loops.
Options A, B, and C are all part of what an array must track to function correctly: the array must know where its data is, how it is laid out (shape/strides), and how to interpret bytes (dtype). In contrast, anencryption key is not a concept that belongs to the internal representation of a numerical array. Encryption is a security mechanism applied at storage or transport layers (for example, encrypting a file on disk or encrypting data sent over a network), not something built into the in-memory structure of a NumPy array object.
Therefore, the aspect excluded from a NumPy array's structure is the encryption key.
NEW QUESTION # 18
What is the time complexity of a quicksort algorithm?
Answer: C
Explanation:
Quicksort is a divide-and-conquer sorting algorithm. It works by selecting a pivot element, partitioning the array into two subarrays (elements less than the pivot and elements greater than the pivot), and then recursively sorting those subarrays. In the average case, the partition step splits the array into roughly equal halves, so the recurrence is commonly written as (T(n) = T(n/2) + T(n/2) + O(n)), where (O(n)) is the cost of partitioning. This solves to (O(n \log n)), which is why quicksort is widely taught as an efficient general- purpose sorting method.
However, textbooks also emphasize that quicksort has a worst-case time complexity of (O(n
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