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| Section | Objectives |
|---|---|
| Topic 1: Basic Program Design | - Explain how to store, access, and manipulate data in lists - Use functions, methods, and packages to leverage programming language - Identify variables and data types within a programming language |
| Topic 2: OS Fundamentals | - Identify common privacy and security concepts that could be implemented in operating systems - Demonstrate various techniques and tools to manage operating systems - Describe fundamental principles and core concepts of operating systems |
| Topic 3: Data Profiling | - Utilize a programming language to manipulate arrays and discover insights - Apply fundamental concepts and subsetting techniques to a dataset |
| 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 # 20
Which line of code below contains an error in the use of NumPy?
Answer: B
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 # 21
Which brand of Type 1 hypervisor is commonly used to create virtual machines?
Answer: D
Explanation:
AType 1 hypervisor, also called abare-metal hypervisor, runs directly on the host machine's hardware rather than on top of a general-purpose operating system. This design is widely described in virtualization textbooks because it improves performance and isolation: the hypervisor controls CPU scheduling, memory management, and I/O virtualization with minimal overhead from an intermediate OS layer. Type 1 hypervisors are therefore common in servers and data centers.
Among the options,VMware ESXiis the well-known Type 1 hypervisor product. It is installed directly onto physical server hardware and provides the virtualization layer used to run multiple virtual machines. In contrast, Parallels Desktop, VirtualBox, and VMware Workstation are typically categorized asType 2 hypervisors, meaning they run as applications on top of a host operating system like Windows, macOS, or Linux. Type 2 hypervisors are excellent for desktops, development, testing, and learning, but they generally rely on the host OS for device drivers and resource management, which can add overhead.
This distinction matters in practice: data centers favor Type 1 hypervisors for efficiency, centralized management, and robust isolation between workloads. Desktop users often choose Type 2 hypervisors for convenience and easier installation. Therefore, the commonly used Type 1 hypervisor brand listed here is VMware ESXi.
NEW QUESTION # 22
What is the output of print(employees[3]) when employees = ["Anika", "Omar", "Li", "Alex"]?
Answer: C
Explanation:
Python lists are ordered sequences indexed starting from 0. This zero-based indexing is standard in many programming languages and is a core concept in data structures. For the list `employees = ["Anika", "Omar",
"Li", "Alex"]`, the mapping of indices to elements is: index 0 # "Anika", index 1 # "Omar", index 2 # "Li", index 3 # "Alex". Therefore, the expression `employees[3]` selects the element at index 3, which is `"Alex"`, and `print(employees[3])` outputs `Alex` (strings print without quotes in normal output).
Option A would be correct for `employees[1]`, option D would be correct for `employees[2]`, and option C would be correct for `employees[0]`. This kind of question tests understanding of list indexing, which is essential for iteration, slicing, and algorithm implementation.
# Textbooks also note the difference between indexing and slicing: indexing returns a single element, while slicing returns a sublist. Here, because square brackets contain a single integer index, it is indexing. If you attempted an index that is out of range, Python would raise an `IndexError`, which reinforces careful reasoning about list length and positions. Understanding these fundamentals is critical for correctly manipulating datasets, where row/column positions and offsets frequently matter.
NEW QUESTION # 23
What Python code would return the value 40 from np_2d, where np_2d = np.array([[1, 2, 3, 4], [10, 20, 30,
40]])?
Answer: C
Explanation:
In a 2D NumPy array, indexing is written as array[row_index, column_index] using zero-based indices. The array np_2d = np.array([[1, 2, 3, 4], [10, 20, 30, 40]]) has two rows (indices 0 and 1) and four columns (indices 0, 1, 2, 3). The value 40 is located in the second row and the fourth column. Using zero-based indexing, that corresponds to row index 1 and column index 3. Therefore, np_2d[1, 3] returns 40.
Option A attempts to access row 3, which does not exist and would raise an IndexError. Option C attempts to access column 4 in row 0, but valid column indices are only 0 through 3, so it would also error. Option D likewise refers to a non-existent row 4. Only option B uses valid indices and points to the correct location.
Textbooks emphasize multi-dimensional indexing because it underlies matrix operations, dataset manipulation, and feature extraction in data science. Correctly interpreting rows and columns is essential when rows represent observations (like people) and columns represent attributes (like age, weight, height). This question tests precise control over row/column addressing, which prevents subtle bugs in numerical analysis.
NEW QUESTION # 24
What happens if one element of a NumPy array is changed to a string?
Answer: A
Explanation:
A central rule in NumPy is that an ndarray has a single, fixed data type called itsdtype. That dtype is chosen when the array is created (for example, int64, float64, etc.), and it normally does not change just because you assign a new value into one element. When you attempt an assignment, NumPy tries tocastthe assigned value into the array's existing dtype. If the cast is possible, the assignment succeeds; if the cast is impossible, NumPy raises an error.
So, if you have a numeric array such as arr = np.array([1, 2, 3]), its dtype is an integer type. Trying arr[0] =
"hello" cannot be converted into an integer, so NumPy raises a ValueError (a casting/conversion error). This is exactly the behavior textbooks highlight when contrasting NumPy arrays with Python lists: lists can hold mixed types freely, but NumPy arrays trade that flexibility for speed and memory efficiency via uniform typing.
Option A is a common misconception. While NumPy may "upcast" values to a more general dtype at array creation time when mixed types are provided (e.g., numbers and strings in the same constructor), a pre-existing numeric array will not automatically convert itself into a string array during a single- element assignment. Options C and D do not reflect NumPy's assignment rules.
NEW QUESTION # 25
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