最新のFoundations-of-Computer-Science認証試験問題集、最適なWGU Foundations-of-Computer-Science試験過去問

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

SectionObjectives
Topic 1: Computer Science Fundamentals- Core CS Concepts
  • 1. Basic programming logic and algorithms
    • 2. Algorithm efficiency and Big-O basics
      • 3. Computational thinking and problem solving
        - Data Structures Introduction
        • 1. Basic sorting and searching concepts
          • 2. Arrays and lists
            Topic 2: Data & Security Basics- Security Fundamentals
            • 1. Basic cybersecurity threats and mitigation
              • 2. Encryption basics (at rest vs in transit)
                - Data Handling
                • 1. Basic database concepts overview
                  • 2. Data profiling concepts
                    Topic 3: Programming Foundations- Programming Concepts
                    • 1. Basic pseudocode interpretation
                      • 2. Control flow (if/else, loops)
                        • 3. Variables, data types, expressions
                          - Language Concepts Overview
                          • 1. Programming paradigms overview
                            • 2. Compiled vs interpreted languages
                              Topic 4: Operating Systems & Architecture- System Architecture
                              • 1. Hardware vs software abstraction
                                • 2. Von Neumann architecture basics
                                  - OS Fundamentals
                                  • 1. Process states and scheduling basics
                                    • 2. Memory management concepts

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                                      WGU Foundations of Computer Science 認定 Foundations-of-Computer-Science 試験問題 (Q15-Q20):

                                      質問 # 15
                                      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：A


                                      質問 # 16
                                      What happens if one element of a NumPy array is changed to a string?

                                      正解：D

                                      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 # 17
                                      Which method converts the default smallest-to-largest index order of a list to instead be the opposite?

                                      正解：C

                                      解説：
                                      Python lists maintain an order, and sometimes you need to reverse that order so the last element becomes first and the first becomes last. The standard list method for reversing the elementsin placeis reverse(). For example, if nums = [1, 2, 3, 4], then nums.reverse() mutates the list so it becomes [4, 3, 2, 1]. This is a built-in operation taught in introductory programming texts because it is efficient and conceptually simple: it does not create a new list unless you explicitly copy the data.
                                      It is important to distinguish reversing from sorting. Reversing changes the sequence order as-is, while sorting rearranges elements according to comparisons. The question refers to converting the index order to the opposite, which is reversing. If you wanted descendingsortedorder, you would typically use sort (reverse=True) or sorted(nums, reverse=True). But the direct method that reverses the list's order is reverse().
                                      The other options are not standard Python list methods. sortDescending(), flip(), and invert() are not part of Python's built-in list API. Textbooks emphasize learning the correct method names because Python's standard library provides a consistent, widely used interface across programs. Thus, reverse() is the correct answer for reversing the index order of a list.


                                      質問 # 18
                                      What type of encryption is provided by encryption utilities built into the file system?

                                      正解：A

                                      解説：
                                      File system encryption utilities are designed to protect datastored on a disk-for example, files on an SSD, HDD, or other persistent storage. This protection is calledencryption at rest. The key idea is that if an attacker steals the physical drive, gains access to a powered-off machine, or otherwise reads storage directly, the raw bytes on disk remain unreadable without the correct cryptographic key. Common textbook examples include full-disk encryption and per-file encryption supported by operating systems and file systems.
                                      This differs fromencryption in motion(also called encryption in transit), which protects data while it is being transmitted over networks, such as via TLS/HTTPS, VPNs, or secure messaging protocols. File system utilities do not primarily address network transmission; they address stored data confidentiality. Option B,
                                      "encryption authentication," is not a standard category; authentication is a security goal often achieved using mechanisms like digital signatures, MACs, certificates, and protocol handshakes, not a type of file system encryption. Option D, steganography, is the practice of hiding information within other data (like images or audio) rather than encrypting it for confidentiality.
                                      In short, file system encryption utilities aim to ensure that stored files remain confidential if storage is accessed without authorization, which is precisely the definition of encryption at rest.


                                      質問 # 19
                                      What is a key advantage of using NumPy when handling large datasets?

                                      正解：C

                                      解説：
                                      NumPy's key advantage for large datasets isefficient storage and fast computation. Unlike Python lists, which store references to objects and can have per-element overhead, NumPy arrays store data in a compact, homogeneous format (single dtype) in contiguous or strided memory. This reduces memory usage and improves cache locality, which is crucial for performance on large arrays. Additionally, NumPy operations are vectorized: many computations run in optimized compiled code rather than interpreted Python loops. This enables large speedups for arithmetic, linear algebra, statistics, and transformations over entire arrays.
                                      Option A is incorrect because NumPy itself does not provide full machine learning algorithms; those are typically found in libraries like scikit-learn, though they build on NumPy. Option B is incorrect because NumPy does not automatically clean data; data cleaning is usually done with pandas or custom logic. Option D is incorrect because interactive visualizations are typically handled by libraries like matplotlib, seaborn, or plotly, not by NumPy.
                                      Textbooks in scientific computing highlight that NumPy forms the computational foundation of the Python data ecosystem. Its array model supports broadcasting, slicing, and efficient aggregations, all of which are essential when working with millions of numeric values. By combining compact memory layout with compiled numerical kernels, NumPy enables scalable analysis and simulation workloads that would be slow or memory-heavy using pure Python lists.


                                      質問 # 20
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