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| Section | Objectives |
|---|---|
| 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 |
| Algorithm Efficiency | - Choose an appropriate sorting algorithm method based on a given scenario - Choose an appropriate algorithm searching method based on a given scenario - Describe the relationships between algorithm complexity and data structures |
| OS Fundamentals | - Describe fundamental principles and core concepts of operating systems - Demonstrate various techniques and tools to manage operating systems - Identify common privacy and security concepts that could be implemented in operating systems |
| Data Profiling | - Utilize a programming language to manipulate arrays and discover insights - Apply fundamental concepts and subsetting techniques to a dataset |
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NEW QUESTION # 10
Which Python command can be used to display the results of calculations?
Answer: D
Explanation:
In Python, the standard way to display output to the console is the built-in function print(). When a program performs calculations-such as arithmetic expressions, function results, or computed statistics-print() can be used to show those results to the user. For example, print(2 + 3) displays 5, and print(total / count) displays the computed average. Textbooks introduce print() early because it supports interactive learning, debugging, and communicating program behavior.
print() can display one or multiple items separated by commas, automatically converting them to string form.
It also supports formatting via f-strings (e.g., print(f"Sum = {s}")) and optional parameters like sep and end to control output formatting. This makes it versatile for reporting calculated values, intermediate steps in algorithms, and final program outputs.
The other options are not standard Python built-ins for output. compute(), result(), and solve() are not universally defined commands in Python; they might exist as user-defined functions or in specific libraries, but they are not the general command taught in textbooks for displaying results. Python follows a clear separation: expressions compute values; print() displays them.
Therefore, the correct answer is print(), as it is the primary mechanism for producing human-readable output from calculations in typical Python programs and coursework.
NEW QUESTION # 11
What will be the result of performing the slice fam[:3]?
Answer: A
Explanation:
Python slicing uses the notation sequence[start:stop], where start is inclusive and stop is exclusive. When start is omitted, it defaults to 0, meaning the slice starts from the beginning of the sequence. Therefore, fam[:3] is equivalent to fam[0:3]. Because the stop index 3 is excluded, the slice includes elements at indices 0, 1, and
2-exactly the first three elements.
This convention is emphasized in programming textbooks because it makes many tasks natural and reduces boundary errors. For example, "take the first n items" is written as [:n], and "drop the first n items" is written as [n:]. The length of the slice is also easy to reason about: with step 1, it is stop - start, so here it is 3 - 0 = 3.
Option B is incorrect because including four elements would require fam[:4]. Option C would correspond to fam[:2]. Option D describes taking elements from the end, which would use negative indexing such as fam
[-3:].
Slicing is widely used for batching, windowing in algorithms, splitting datasets into training/testing segments, and extracting prefixes in parsing tasks. Understanding the inclusive start and exclusive stop rule is essential for correct Python programming.
NEW QUESTION # 12
What is the expected output of numpy_array[1]?
Answer: A
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 # 13
Which line of code below contains an error in the use of NumPy?
Answer: D
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 # 14
The np_2d array stores information about multiple family members. Each row represents a different person, and the columns store family member attributes in the following order:
Age (years)
Weight (pounds)
Height (inches)
How is the weight of all family members selected from the np_2d array?
Answer: A
Explanation:
In a 2D NumPy array, rows and columns represent different dimensions of the data. The indexing form array
[row_selection, column_selection] allows you to select entire rows, entire columns, or submatrices. The slice :
means "all indices along this dimension." Since each row corresponds to a family member (a person), selecting weights forallfamily members means selectingall rowsfor the weight column.
The problem states the columns are ordered as: Age (column 0), Weight (column 1), Height (column 2).
Therefore, the weight column has index 1. The expression np_2d[:, 1] uses : to take every row and 1 to take the second column, producing a 1D array (or a column view) containing the weight values for all people.
Option A, np_2d[:, 2], would select the height column, not weight. Option C, np_2d[2, :], selects the third row (the third person) and all columns-age, weight, and height for just that one person. Option D, np_2d[1, :], selects the second person's entire row.
This column selection technique is fundamental in data analysis because datasets are often stored as
"rows = observations, columns = features," and extracting a feature vector is a frequent operation before computing statistics or building models.
NEW QUESTION # 15
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