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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Structures | 20% | - Data storage and retrieval principles - Primitive and composite data types - Trees, graphs, hash tables - Arrays, linked lists, stacks, queues |
| Topic 2: Algorithms & Complexity | 25% | - Recursion and iterative structures - Big O notation, time and space complexity - Sorting and searching algorithms - Algorithm design and analysis |
| Topic 3: Computer Architecture & Organization | 15% | - Instruction sets and execution cycles - Memory hierarchy and performance - Von Neumann architecture - CPU, memory, I/O systems |
| Topic 4: Software Engineering & Programming Basics | 15% | - Software development lifecycle - Testing and debugging fundamentals - Basic syntax and control structures - Programming paradigms |
| Topic 5: Discrete Mathematics & Logic | 25% | - Proof techniques and mathematical induction - Propositional and predicate logic - Boolean algebra and digital logic - Set theory, relations, functions |
>> Foundations-of-Computer-Science日本語的中対策 <<
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質問 # 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?
正解：C
解説：
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.
質問 # 23
What happens if one element of a NumPy array is changed to a string?
正解：B
解説：
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.
質問 # 24
How can a user subset a NumPy array bmi to only include values over 23?
正解：C
解説：
NumPy supports a powerful technique calledBoolean indexing(also called Boolean masking) to filter arrays based on a condition. When you write bmi > 23, NumPy performs an element-wise comparison and produces a Boolean array of the same shape, containing True where the condition holds and False otherwise. Using that Boolean array inside square brackets, as in bmi[bmi > 23], tells NumPy to return a new 1D array containing only the elements whose mask value is True. This approach is heavily emphasized in scientific computing curricula because it expresses selection logic without explicit loops and runs efficiently in optimized compiled code.
Option B looks close but is not standard NumPy usage. The function commonly used is np.where(condition) or np.where(condition, x, y). While np.where(bmi > 23) can return indices, bmi.where(...) is not a NumPy array method; it is more associated with pandas objects. Options A and C are not valid NumPy APIs for filtering.
Boolean indexing is central in data analysis tasks such as removing invalid measurements, selecting a population subgroup, applying thresholds, and building feature subsets. It composes cleanly with vectorized computation, for example bmi[bmi > 23].mean(), enabling concise and high-performance numerical workflows.
質問 # 25
Given the following code, what is the expected output?
正解：A
解説：
In NumPy, a 2D array can be visualized as a table of rows and columns. When you write np_2d[0], you are usingzero-based indexingto select thefirst rowof that 2D array. This is a standard convention in Python and many other programming languages: index 0 refers to the first element, index 1 to the second, and so on.
Therefore, np_2d[0] returns all the elements in row 0.
With a typical construction such as np_2d = np.array([[1, 2, 3, 4], [10, 20, 30, 40]]), the first row is [1, 2, 3,
4], so printing np_2d[0] displays that row. NumPy returns the row as a 1D NumPy array, and when printed it often appears in bracket form like [1 2 3 4] (spaces rather than commas are common in NumPy's display).
Conceptually, however, the contents are exactly the first row values, matching option C.
Option A and D show the second row (index 1), not the first. Option B incorrectly suggests a column extraction rather than a row selection.
質問 # 26
Which type of sorting algorithm starts at the first position and moves the pointer until the end of the list, determining the lowest value?
正解：C
解説：
Selection sort is the algorithm that repeatedly scans the unsorted portion of a list to find the lowest (or highest) value and then places it into its correct position in the sorted portion. It begins at the first index (position 0) and treats that as the boundary between sorted and unsorted regions. On the first pass, it moves a scanning pointer through the entire list to determine the minimum element and swaps it into position 0. On the second pass, it starts from position 1, scans to the end to find the next minimum, and swaps it into position 1.
This continues until the list is sorted.
This matches the question's description: "starts at the first position and moves the pointer until the end of the list, determining the lowest value." Textbooks often describe selection sort with two indices: one for the current boundary position and one for scanning the remainder of the list to find the minimum. The algorithm is simple and uses O(1) extra space, but it is inefficient for large lists because it performs O(n²) comparisons regardless of input order.
The other options are not standard algorithm names in typical computer science curricula. While many sorting algorithms exist (insertion sort, merge sort, quicksort, heap sort), "incremental," "progressive," and "pointer sort" are not canonical textbook algorithms in this context. Therefore, the correct answer is selection sort.
質問 # 27
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