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| Section | Objectives |
|---|---|
| Topic 1: 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 2: Algorithm Efficiency | - Describe the relationships between algorithm complexity and data structures - Choose an appropriate algorithm searching method based on a given scenario - Choose an appropriate sorting algorithm 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: 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 # 35
What statistical measure can be used to detect outliers in a dataset using NumPy?
Answer: B
Explanation:
Outlier detection often relies on measuring how far values deviate from a "typical" center. While variance and standard deviation can be used in simple z-score based methods, they arenot robust: a few extreme outliers can inflate the mean and standard deviation, masking the very outliers you want to find. A widely taught robust alternative is themedian absolute deviation (MAD), which is based on the median rather than the mean and therefore resists distortion by extreme values.
MAD is computed by first taking the median of the data, then computing the absolute deviation of each point from that median, and finally taking the median of those deviations. Because medians are stable under extreme values, MAD provides a strong baseline for identifying unusually distant points. Many textbooks and data analysis references present MAD as a robust scale estimator for outlier detection, often combined with a threshold rule such as flagging points whose deviation exceeds a constant multiple of MAD (with a scaling factor sometimes used to make it comparable to standard deviation under normality assumptions).
In NumPy, you can implement MAD using np.median() and np.abs(). Mode is generally not useful for continuous numeric outlier detection, and variance/standard deviation are more sensitive to outliers than MAD. Thus, among the given options, the best statistical measure for detecting outliers robustly is the median absolute deviation.
NEW QUESTION # 36
What is the alternative way to access the third element of the first row in np_2d?
Answer: A
Explanation:
NumPy arrays use zero-based indexing, meaning counting starts at 0 rather than 1. In a 2D NumPy array, indexing is typically written in the form array[row_index, column_index]. The first index selects the row, and the second index selects the column. Therefore, the "first row" corresponds to row index 0. Within that row, the "third element" corresponds to column index 2, because the columns are indexed 0, 1, 2, 3, and so on.
So, np_2d[0, 2] directly selects the element at row 0 and column 2, which is the third element in the first row.
This is considered an "alternative" to approaches like two-step indexing (np_2d[0][2]), and it is the standard idiom taught for multi-dimensional NumPy arrays.
The other choices point to different locations. np_2d[1, 3] is the fourth element of the second row, not the third element of the first row. np_2d[2, 0] and np_2d[3, 1] attempt to access the third or fourth row, which would often be out of bounds in a small 2-row example and would raise an IndexError. Correct indexing is a cornerstone of array programming because it determines which observation, feature, or matrix entry your computations will use.
NEW QUESTION # 37
Which aspect is excluded from a NumPy array's structure?
Answer: B
Explanation:
A NumPy ndarray is designed for efficient numerical computing, and its structure is defined by metadata required to interpret a contiguous (or strided) block of memory as an n-dimensional array. Textbooks and NumPy's own conceptual model describe key components such as: adata buffer(where the raw bytes live), a data pointer(reference to the start of that buffer), thedtype(which specifies how to interpret each element's bytes-e.g., int32, float64), theshape(the size in each dimension), andstrides(how many bytes to step in memory to move along each dimension). Together, these allow fast indexing, slicing, and vectorized operations without Python-level loops.
Options A, B, and C are all part of what an array must track to function correctly: the array must know where its data is, how it is laid out (shape/strides), and how to interpret bytes (dtype). In contrast, anencryption key is not a concept that belongs to the internal representation of a numerical array. Encryption is a security mechanism applied at storage or transport layers (for example, encrypting a file on disk or encrypting data sent over a network), not something built into the in-memory structure of a NumPy array object.
Therefore, the aspect excluded from a NumPy array's structure is the encryption key.
NEW QUESTION # 38
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 # 39
What is the main advantage of using NumPy arrays over regular Python lists for data analysis?
Answer: B
Explanation:
The primary advantage of NumPy arrays in data analysis is their support for fast, vectorized computation over whole collections of numeric data. A NumPy `ndarray` stores elements in a contiguous memory block with a single, fixed data type, enabling efficient low-level operations implemented in optimized C/Fortran code. As a result, expressions like `arr + 5`, `arr * arr`, or `np.mean(arr)` operate over the entire array without explicit Python loops. This style is commonly called **vectorization**, and it is a central theme in scientific computing textbooks because it is both clearer to read and significantly faster for large datasets.
Option A describes a property of Python lists, not NumPy arrays. Python lists can mix types freely, but this flexibility comes with overhead. Option B is true-NumPy arrays typically hold a single dtype-but it is not the main advantage; it is more of an implementation feature that enables speed and memory efficiency.
Option D is not a defining advantage; both lists and arrays can be concatenated, and NumPy provides dedicated functions such as `np.concatenate`, but concatenation is not the core reason NumPy dominates data analysis workflows.
# Because NumPy operations are applied element-wise across entire arrays and can leverage CPU vector instructions and efficient memory access patterns, they form the foundation for higher-level tools like pandas, SciPy, and many machine learning libraries. This is why the best answer is that NumPy arrays can perform calculations over entire collections of values.
NEW QUESTION # 40
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