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| Section | Objectives |
|---|---|
| Topic 1: Data Profiling | - Utilize a programming language to manipulate arrays and discover insights - Apply fundamental concepts and subsetting techniques to a dataset |
| Topic 2: 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 3: OS Fundamentals | - Describe fundamental principles and core concepts of operating systems - Identify common privacy and security concepts that could be implemented in operating systems - Demonstrate various techniques and tools to manage operating systems |
| Topic 4: Basic Program Design | - Identify variables and data types within a programming language - Use functions, methods, and packages to leverage programming language - Explain how to store, access, and manipulate data in lists |
>> Questions Foundations-of-Computer-Science Pdf <<
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NEW QUESTION # 12
What is the likely cause if a default Python configuration does not recognize a NumPy array as an allowed data structure?
Answer: C
Explanation:
NumPy arrays are not a built-in Python data structure. In a default Python installation, the interpreter includes core types such as int, float, str, list, tuple, dict, and set, plus the standard library. A NumPy array, typically created as numpy.ndarray, is provided by the third-party NumPy library. Therefore, if a "default Python configuration" does not recognize a NumPy array, the most likely cause is thatNumPy is not installed or not available in the active environment. This happens often when a user has multiple Python environments (system Python, virtual environments, conda environments) and installs NumPy into one environment while running code in another.
Option B is incorrect because Python's standard-library array module is different from NumPy. Importing array does not create or enable NumPy's ndarray type. Option C is possible in rare cases,but the typical, textbook-aligned explanation is missing dependencies rather than an incorrectly configured interpreter. Option D is also unlikely: while very old Python versions may cause compatibility issues with modern NumPy releases, the symptom described-NumPy arrays not being recognized at all-more directly indicates the package is absent in the running environment.
In practice, verifying import numpy and checking the installed packages for the current interpreter resolves the issue.
NEW QUESTION # 13
What is the time complexity of a quicksort algorithm?
Answer: D
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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