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| Section | Objectives |
|---|---|
| Topic 1: Algorithm Efficiency | - Describe the relationships between algorithm complexity and data structures - Choose an appropriate sorting algorithm method based on a given scenario - Choose an appropriate algorithm searching method based on a given scenario |
| Topic 2: Data Profiling | - Utilize a programming language to manipulate arrays and discover insights - Apply fundamental concepts and subsetting techniques to a dataset |
| Topic 3: OS Fundamentals | - Demonstrate various techniques and tools to manage operating systems - Describe fundamental principles and core concepts of operating systems - Identify common privacy and security concepts that could be implemented in operating systems |
| Topic 4: Basic Program Design | - Use functions, methods, and packages to leverage programming language - Explain how to store, access, and manipulate data in lists - Identify variables and data types within a programming language |
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질문 # 19
Which line of code below contains an error in the use of NumPy?
정답：B
설명：
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.
질문 # 20
What is the time complexity of a quicksort algorithm?
정답：B
설명：
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