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

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

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                                      WGU Foundations of Computer Science Sample Questions (Q60-Q65):

                                      NEW QUESTION # 60
                                      Which statement describes the relationship between trees and graphs?

                                      Answer: A

                                      Explanation:
                                      In discrete mathematics and computer science, atreeis a special kind ofgraph. The standard graph-theory definition is that a tree is aconnected, acyclicundirected graph. "Acyclic" means it containsno cycles, i.e., you cannot start at a vertex, follow a sequence of edges, and return to the starting vertex without repeating edges in a way that forms a loop. (Wikipedia) This property is exactly what makes option D correct.
                                      The other options contradict the definition. If a structure has cycles, it is not a tree (though it may still be a graph). If it has unconnected nodes, it is not connected; such a structure is more like aforest(a disjoint union of trees) rather than a single tree. (Wikipedia) The idea of "levels" belongs to a particular computer-science representation called arooted tree, where one node is chosen as the root and nodes can be assigned depths
                                      /levels based on distance from the root. But levels are not required in the abstract definition of a tree as a graph; they arise from choosing a root and orientation for convenience in algorithms like BFS/DFS, heaps, and parse trees.
                                      So, the relationship is: every tree is a graph with extra structure-specifically, no cycles and (typically) connectivity-and the "no cycles" rule is the key distinguishing feature. (Discrete Mathematics)


                                      NEW QUESTION # 61
                                      Which sorting algorithm works by finding the smallest or largest element in an unsorted part of a list and moving it to the sorted part of the list?

                                      Answer: B

                                      Explanation:
                                      Selection sort is defined by a simple repeated strategy: divide the list into a sorted region and an unsorted region, then repeatedly select the smallest (or largest) element from the unsorted region and move it to the end of the sorted region. In the common "smallest-first" version, the algorithm scans the unsorted portion to find the minimum element, then swaps it into the next position in the sorted portion. After the first pass, the smallest element is fixed at index 0; after the second pass, the second-smallest is fixed at index 1; and so on until the entire list is sorted.
                                      This exactly matches the description in the question, making selection sort the correct answer. Textbooks often use selection sort to teach algorithmic thinking because it is easy to understand and implement, though not efficient for large datasets. Its time complexity is O(n²) in the average and worst case because it performs roughly n scans of progressively smaller unsorted sections, with each scan taking linear time. Its space usage is O(1) additional space because it sorts in place using swaps.
                                      The other options do not match the described mechanism. Quicksort partitions around a pivot, heap sort uses a heap data structure to repeatedly extract the maximum/minimum, and radix sort processes digits/keys by place value rather than selecting minima by scanning. Selection sort's defining action is the repeated "select the min/max and place it."


                                      NEW QUESTION # 62
                                      Which type of files are meant to be inaccessible to standard users, but can be critical in terms of functionality?

                                      Answer: A

                                      Explanation:
                                      Operating systems contain many files that are essential for booting, hardware support, security enforcement, and core services. These are generally referred to assystem files. Textbooks explain that system files are often protected by permissions and special attributes because accidental modification or deletion could destabilize the OS, break device drivers, prevent applications from running, or even stop the machine from booting.
                                      Therefore, standard (non-administrator) users are typically restricted from accessing or altering them, and the OS may hide them by default to reduce the risk of user error.
                                      Examples include kernel-related components, shared libraries, driver files, configuration databases, and critical service executables. Modern OS designs enforce protection through user accounts, access control lists, and privilege separation. This ensures only trusted processes and administrators can change system-critical components.
                                      Log files record events and are sometimes protected, but many logs are readable by users or administrators depending on policy; they are not necessarily "meant to be inaccessible" in the same strict sense. Backup files are important for recovery but are not inherently system-critical for day-to-day operation, and their accessibility depends on organizational policy. "Extension files" is not a standard category; file extensions describe formats rather than a protected functional class.
                                      Thus, the files intended to be inaccessible to standard users yet critical for functionality are system files, reflecting core OS security principles such as least privilege and integrity protection.


                                      NEW QUESTION # 63
                                      What is traversal in the context of trees and graphs?

                                      Answer: B

                                      Explanation:
                                      In data structures and algorithms,traversalrefers to systematicallyvisiting nodesin a tree or graph in order to process them. "Visiting" typically means performing some operation at each node, such as reading its value, marking it as seen, computing a property, or collecting it into an output structure. Traversal is foundational because many algorithms-search, path finding, connectivity checks, topological analysis, and evaluation of expressions-are built on traversal patterns.
                                      Intrees, traversal has classic forms: preorder, inorder, and postorder depth-first traversals, as well as breadth- first traversal (level-order). Each defines a rule for the order in which nodes are visited relative to their children. Ingraphs, traversal must additionally handle the possibility of cycles and multiple paths; textbooks therefore emphasize maintaining a "visited" set to avoid infinite loops. The two principal graph traversal strategies areDepth-First Search (DFS)andBreadth-First Search (BFS). DFS explores along a path as far as possible before backtracking, while BFS explores layer by layer outward from a start node.
                                      Options A, B, and C do not define traversal. Changing values may happen during traversal, but it is not what traversal means. Removing all nodes is deletion, not traversal. Connecting all nodes is not a standard traversal concept. The correct definition is the process of visiting all nodes (typically reachable from a starting node, or all nodes in the structure if fully connected).


                                      NEW QUESTION # 64
                                      How is a NumPy array named data with 6 elements reshaped into a 2x3 array?

                                      Answer: D

                                      Explanation:
                                      Reshaping is the operation of changing the "view" of an array so that the same elements are arranged with new dimensions. In NumPy, reshaping is possible when the total number of elements stays the same. A 2x3 array contains 6 elements, so a 1D array data of length 6 can be reshaped into shape (2, 3) without adding or removing values. Textbooks stress this invariant: the product of the dimensions must equal the original size.
                                      NumPy provides two standard reshaping interfaces: the function np.reshape(data, (2, 3)) and the method data.
                                      reshape(2, 3) (or data.reshape((2, 3))). Option A is correct because it uses the official NumPy function with the proper arguments: the original array and the target shape. The shape is passed as a tuple describing rows and columns.
                                      Option B is incorrect because np_reshape is not the correct NumPy function name, and it references an unrelated identifier list. Option C is incorrect because NumPy arrays do not provide a set_shape method like that. Option D is not valid NumPy syntax for reshaping.
                                      Reshaping is fundamental in data analysis and machine learning: it converts flat vectors into matrices, prepares batches of samples, and aligns dimensions for matrix multiplication and broadcasting.


                                      NEW QUESTION # 65
                                      ......

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