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WGU Foundations-of-Computer-Science Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Computer Architecture & Organization15%- Memory hierarchy and performance
- Von Neumann architecture
- Instruction sets and execution cycles
- CPU, memory, I/O systems
Topic 2: Data Structures20%- Primitive and composite data types
- Arrays, linked lists, stacks, queues
- Data storage and retrieval principles
- Trees, graphs, hash tables
Topic 3: Software Engineering & Programming Basics15%- Testing and debugging fundamentals
- Basic syntax and control structures
- Programming paradigms
- Software development lifecycle
Topic 4: Algorithms & Complexity25%- Recursion and iterative structures
- Big O notation, time and space complexity
- Algorithm design and analysis
- Sorting and searching algorithms
Topic 5: Discrete Mathematics & Logic25%- Boolean algebra and digital logic
- Propositional and predicate logic
- Set theory, relations, functions
- Proof techniques and mathematical induction

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Foundations-of-Computer-Scienceコンポーネント & Foundations-of-Computer-Science専門知識内容

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WGU Foundations of Computer Science 認定 Foundations-of-Computer-Science 試験問題 (Q26-Q31):

質問 # 26
What is the name of the tool that can allow a device to run more than one operating system at a time as virtual machines?

正解:B

解説:
Ahypervisoris the software layer that enables virtualization-running multiple operating systems concurrently on the same physical hardware as separate, isolated virtual machines (VMs). Operating systems textbooks describe the hypervisor as managing and multiplexing core hardware resources such as CPU, memory, storage, and I/O devices among multiple guest operating systems. Each VM behaves as if it has its own hardware, while the hypervisor enforces isolation and schedules resource usage.
Hypervisors come in two broad categories.Type 1 (bare-metal)hypervisors run directly on the hardware (common in data centers), whileType 2 (hosted)hypervisors run as applications on top of a host OS (common on desktops). In both cases, the hypervisor is the key tool that makes "more than one OS at a time" possible.
System Restore is a recovery feature, not a virtualization platform. A partition manager can split a disk into multiple partitions, which can support dual-boot setups, but that runs only one OS at a time, not concurrently as VMs. A bootloader selects which OS to start at boot time; again, that is not simultaneous virtualization. Therefore, the correct tool that allows running multiple operating systems simultaneously as virtual machines is the hypervisor.


質問 # 27
What is the first step in the selection sort algorithm?

正解:D

解説:
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.


質問 # 28
What stores the location of the next node in a linked list?

正解:B

解説:
A linked list is a dynamic data structure made up of nodes, where each node typically contains two components: a data field (the value being stored) and a link field (commonly called a pointer or reference).
The pointer's role is to store the memory address (or reference) of the next node in the sequence, thereby maintaining the logical order of the list even though nodes may be scattered throughout memory. This is a key contrast with arrays, which store elements contiguously and rely on index arithmetic to locate the next element.
Because each node explicitly points to the next node, linked lists support efficient insertion and deletion operations compared with arrays. To insert a node, you allocate it and then adjust pointers so it fits into the chain. To delete a node, you redirect the pointer of the previous node to skip over the removed node.
Traversal is performed by starting at the head node and repeatedly following the pointer until a null reference indicates the end of the list.
The other options do not correctly describe what stores the location of the next node. An index is used in array-like structures, not in a standard linked list node. The value is the payload data, not the link.
The "header" (often called the head pointer) is an external reference to the first node, not the field inside each node that links to the next. Therefore, the correct answer is the pointer.


質問 # 29
Which aspect is excluded from a NumPy array's structure?

正解:A

解説:
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.


質問 # 30
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 # 31
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