人気の有るWGU Foundations-of-Computer-Science難易度受験料 &信頼できるJpexam -認定試験のリーダー

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

SectionObjectives
Topic 1: Data & Security Basics- Security Fundamentals
  • 1. Encryption basics (at rest vs in transit)
    • 2. Basic cybersecurity threats and mitigation
      - Data Handling
      • 1. Data profiling concepts
        • 2. Basic database concepts overview
          Topic 2: Operating Systems & Architecture- System Architecture
          • 1. Von Neumann architecture basics
            • 2. Hardware vs software abstraction
              - OS Fundamentals
              • 1. Process states and scheduling basics
                • 2. Memory management concepts
                  Topic 3: 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 4: Programming Foundations- Language Concepts Overview
                            • 1. Compiled vs interpreted languages
                              • 2. Programming paradigms overview
                                - Programming Concepts
                                • 1. Basic pseudocode interpretation
                                  • 2. Control flow (if/else, loops)
                                    • 3. Variables, data types, expressions

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                                      WGU Foundations of Computer Science 認定 Foundations-of-Computer-Science 試験問題 (Q52-Q57):

                                      質問 # 52
                                      What Python code would return the value 40 from np_2d, where np_2d = np.array([[1, 2, 3, 4], [10, 20, 30,
                                      40]])?

                                      正解：B

                                      解説：
                                      In a 2D NumPy array, indexing is written as array[row_index, column_index] using zero-based indices. The array np_2d = np.array([[1, 2, 3, 4], [10, 20, 30, 40]]) has two rows (indices 0 and 1) and four columns (indices 0, 1, 2, 3). The value 40 is located in the second row and the fourth column. Using zero-based indexing, that corresponds to row index 1 and column index 3. Therefore, np_2d[1, 3] returns 40.
                                      Option A attempts to access row 3, which does not exist and would raise an IndexError. Option C attempts to access column 4 in row 0, but valid column indices are only 0 through 3, so it would also error. Option D likewise refers to a non-existent row 4. Only option B uses valid indices and points to the correct location.
                                      Textbooks emphasize multi-dimensional indexing because it underlies matrix operations, dataset manipulation, and feature extraction in data science. Correctly interpreting rows and columns is essential when rows represent observations (like people) and columns represent attributes (like age, weight, height). This question tests precise control over row/column addressing, which prevents subtle bugs in numerical analysis.


                                      質問 # 53
                                      What is the time complexity of a binary search algorithm?