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| Section | Objectives |
|---|---|
| Topic 1: OS Fundamentals | - Demonstrate various techniques and tools to manage operating systems - Identify common privacy and security concepts that could be implemented in operating systems - Describe fundamental principles and core concepts of operating systems |
| Topic 2: Data Profiling | - Apply fundamental concepts and subsetting techniques to a dataset - Utilize a programming language to manipulate arrays and discover insights |
| Topic 3: Basic Program Design | - Use functions, methods, and packages to leverage programming language - Identify variables and data types within a programming language - Explain how to store, access, and manipulate data in lists |
| Topic 4: 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 |
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NEW QUESTION # 18
What is another term for the inputs into a function?
Answer: A
Explanation:
In programming, a function takes inputs, performs computation, and may return an output. The standard term for a function's inputs isarguments(also commonly discussed alongside the closely related termparameters).
Textbooks typically distinguish the two:parametersare the names listed in the function definition, while argumentsare the actual values supplied when the function is called. For example, in def f(x, y):, x and y are parameters. In the call f(3, 5), 3 and 5 are arguments. Many introductory materials use "arguments" informally to refer to the inputs overall, which matches the wording of this question.
Options A, B, and C do not fit the textbook definition. "Variables" is too broad; inputs can be literals, expressions, or variables, but the conceptual role is "arguments." "Procedures" are callable units of code (often used in some languages to mean functions without return values), not the inputs. "Outputs" refers to returned results, not what you pass in.
Understanding arguments is important because it connects to call semantics, scope, and correctness.
Different languages support positional arguments, keyword arguments, default values, and variadic arguments (e.g., *args, **kwargs in Python). This flexibility shapes API design and influences how programmers structure reusable code.
NEW QUESTION # 19
What are Python functions that belong to specific Python objects?
Answer: C
Explanation:
In object-oriented programming, amethodis a function that is associated with an object (or its class) and is called using the dot operator. In Python, everything is an object, and many operations are provided through methods. For example, "hello".upper() calls the upper method of a str object, and [1, 2, 3].append(4) calls the append method of a list object. Textbooks emphasize that methods operate on an object's internal state and typically receive the object itself as an implicit first argument (commonly named self in class definitions).
This is what distinguishes methods from standalone functions.
Modules, scripts, and libraries are different organizational concepts. Amoduleis a file containing Python code, including function and class definitions. Ascriptis a Python program intended to be run directly. A libraryis a collection of modules that provides reusable functionality. None of these terms specifically mean
"functions that belong to objects."
Understanding methods matters because it connects to encapsulation and abstraction: objects provide behaviors (methods) that manipulate their data in well-defined ways. This design enables clearer APIs and supports polymorphism, where different object types can expose methods with the same name but different implementations. In Python, method calls are central to working with built-in types (strings, lists, dictionaries) and with user-defined classes, making "methods" the correct term for functions that belong to specific objects.
NEW QUESTION # 20
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 # 21
What is the first step in the selection sort algorithm?
Answer: D
Explanation:
Selection sort works by growing a sorted portion of the list one element at a time. The algorithm conceptually divides the array into two regions: asorted prefixon the left and anunsorted suffixon the right. At the beginning, the sorted prefix is empty and the entire list is unsorted. The first step is to consider position 0 as the target location for the smallest element. The algorithm scans the unsorted region (initially the whole list) to find the smallest valueand records its index. That action is exactly what option C describes: determine the lowest value starting from the first position.
After identifying the minimum element, selection sort swaps it into position 0 (if it isn't already there). Then it repeats the process for position 1, scanning the remaining unsorted suffix to find the next smallest element, swapping it into place, and so on. Textbooks emphasize that the key characteristic of selection sort is the repeated "select min (or max) from unsorted region and place it into the sorted region." Option A is not the standard first step; finding both min and max is unnecessary. Option B describes an unrelated swap that doesn't ensure progress toward sorting. Option D is not a "first step" but rather a different ordering goal; selection sort can be adapted for descending order, but the canonical version begins by selecting the minimum for the first position.
NEW QUESTION # 22
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: C
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 # 23
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