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WGU Foundations of Computer Science 認定 Foundations-of-Computer-Science 試験問題 (Q11-Q16):

質問 # 11
What is the purpose of the pointer element of each node in a linked list?

• A. To indicate the current position
• B. To keep track of the list size
• C. To indicate the next node
• D. To store the data value

正解：C

解説：
In a singly linked list, each node is a small record that typically contains two main parts: a data field and a pointer field. The data field stores the actual value being kept in the list. The pointer field stores the address or reference of another node. The pointer element's purpose is to connect one node to the next by indicating where the next node is located in memory. This is essential because linked-list nodes are not stored in contiguous memory locations the way array elements are. Nodes may exist anywhere in memory, and the pointer is what preserves the logical sequence of the list.
This design supports efficient structural changes. For traversal, a program starts at the head node and repeatedly follows the pointer to reach subsequent nodes. For insertion, a new node can be added by adjusting a small number of pointers instead of shifting many elements, as would be required in an array. For deletion, the list can "skip over" a node by updating the pointer in the previous node to reference the node after the removed one. The end of the list is typically represented by a null pointer value, signaling there is no next node.
Keeping track of list size or current position is not the responsibility of each node's pointer field; these are usually handled by separate variables or computed during traversal.

質問 # 12
Which type of data structure is the only focus of a binary search?

• A. Linked list
• B. Stack
• C. Ordered list
• D. Queue

正解：C

解説：
Binary search is designed for searching in asorted (ordered) sequence. Its efficiency comes from repeatedly comparing the target to the middle element and discarding half of the remaining search space. This halving logic only works when the data is ordered, because the algorithm relies on the guarantee that all elements on one side of the midpoint are smaller (or larger) than the midpoint. In textbooks, this requirement is stated explicitly: binary search assumes the collection is sorted according to the same ordering used for comparisons.
An "ordered list" is therefore the correct focus among the options. Binary search can be implemented on arrays or other random-access structures where you can quickly access the middle element by index. While you can conceptually perform binary search on a linked list, it becomes inefficient because finding the middle requires linear traversal, losing the O(log n) advantage. Stacks and queues are not appropriate because they restrict access to ends only (LIFO for stacks, FIFO for queues), preventing direct access to the midpoint and making the binary search strategy infeasible.
Thus, the central requirement for binary search is a sorted/ordered sequence, typically supporting efficient indexing, which is why the correct choice is an ordered list.

質問 # 13
How can a user subset a NumPy array bmi to only include values over 23?

• A. bmi[bmi > 23]
• B. bmi.where(bmi > 23)
• C. bmi.get_values(>23)
• D. bmi.select(23)

正解：A

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 # 14
What is the time complexity of a quicksort algorithm?

• A. O(n log n)
• B. O(1)
• C. O(n)
• D. O(log n)

正解：A

解説：
Quicksort is a divide-and-conquer sorting algorithm. It works by selecting a pivot element, partitioning the array into two subarrays (elements less than the pivot and elements greater than the pivot), and then recursively sorting those subarrays. In the average case, the partition step splits the array into roughly equal halves, so the recurrence is commonly written as (T(n) = T(n/2) + T(n/2) + O(n)), where (O(n)) is the cost of partitioning. This solves to (O(n \log n)), which is why quicksort is widely taught as an efficient general- purpose sorting method.
However, textbooks also emphasize that quicksort has a worst-case time complexity of (O(n

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