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

Section Objectives
Topic 1: Data Profiling - Utilize a programming language to manipulate arrays and discover insights
- Apply fundamental concepts and subsetting techniques to a dataset
Topic 2: OS Fundamentals - Demonstrate various techniques and tools to manage operating systems
- Identify common privacy and security concepts that could be implemented in operating systems
- Describe fundamental principles and core concepts of operating systems
Topic 3: Algorithm Efficiency - Choose an appropriate sorting algorithm method based on a given scenario
- Choose an appropriate algorithm searching method based on a given scenario
- Describe the relationships between algorithm complexity and data structures
Topic 4: Basic Program Design - Explain how to store, access, and manipulate data in lists
- Identify variables and data types within a programming language
- Use functions, methods, and packages to leverage programming language

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Foundations-of-Computer-Science學習筆記 & Foundations-of-Computer-Science下載

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最新的 Courses and Certificates Foundations-of-Computer-Science 免費考試真題 (Q38-Q43):

問題 #38
What is the likely cause if a default Python configuration does not recognize a NumPy array as an allowed data structure?

  • A. The array module is not imported.
  • B. The Python version is outdated.
  • C. The NumPy package is not present.
  • D. The Python interpreter is misconfigured.

答案：C

解題說明：
NumPy arrays are not a built-in Python data structure. In a default Python installation, the interpreter includes core types such as int, float, str, list, tuple, dict, and set, plus the standard library. A NumPy array, typically created as numpy.ndarray, is provided by the third-party NumPy library. Therefore, if a "default Python configuration" does not recognize a NumPy array, the most likely cause is thatNumPy is not installed or not available in the active environment. This happens often when a user has multiple Python environments (system Python, virtual environments, conda environments) and installs NumPy into one environment while running code in another.
Option B is incorrect because Python's standard-library array module is different from NumPy. Importing array does not create or enable NumPy's ndarray type. Option C is possible in rare cases,but the typical, textbook-aligned explanation is missing dependencies rather than an incorrectly configured interpreter. Option D is also unlikely: while very old Python versions may cause compatibility issues with modern NumPy releases, the symptom described-NumPy arrays not being recognized at all-more directly indicates the package is absent in the running environment.
In practice, verifying import numpy and checking the installed packages for the current interpreter resolves the issue.


問題 #39
How does the data type of a variable get set in Python?

  • A. It is chosen randomly.
  • B. It is always set to string by default.
  • C. It is explicitly declared by the programmer.
  • D. It is determined by the value assigned to it.

答案：D

解題說明：
Python usesdynamic typing, a core concept emphasized in programming language textbooks. In dynamically typed languages, a variable name does not permanently "own" a type. Instead, theobjectcreated by an expression has a type, and the variable becomes a reference to that object. Therefore, the type associated with a variable at any moment is determined by the value assigned to it. For example, after x = 7, x refers to an integer object. After x = "seven", the same name now refers to a string object. The type changes because the binding changes, not because the variable's type declaration was edited.
Option A describesstatic typingsystems (common in languages like Java, C, or C++), where programmers declare types and compilers enforce them. Python does not require such declarations for ordinary variables.
Option B is incorrect because type assignment is deterministic, not random. Option C is incorrect because Python does not default variables to strings; it assigns whatever type results from the right-hand-side expression.
This model is closely tied to Python's runtime behavior: type checks occur during execution, and functions can accept values of different types as long as the operations used are valid (often discussed as
"duck typing"). This flexibility supports rapid development, but also motivates careful testing and, in larger systems, optional type hints for documentation and tool support.


問題 #40
What is the expected result of running the following code: list1[0] = "California"?

  • A. The list will be extended by adding "California" at the end.
  • B. The first value in the list will be replaced with "California".
  • C. A new list will be created with the value "California".
  • D. A second element will be added to the line "California".

答案：B

解題說明：
Python lists are mutable sequences, which means elements can be changed in place after the list has been created. The expression list1[0] = "California" uses indexing to target the element at position 0 (the first element, because Python uses zero-based indexing) and assignment (=) to replace that element with a new value. As a result, the list keeps the same length, but its first entry becomes "California".
This operation does not create a new list (so option A is incorrect); it modifies the existing list object referenced by list1. It also does not append to the end of the list (so option C is incorrect). Appending would use methods like list1.append("California"). Option D is not meaningful in Python list semantics; assignment to a single index replaces exactly one element rather than "adding a second element to the line." Textbooks highlight this difference between mutable and immutable sequence types. For example, strings are immutable, so you cannot assign to some_string[0]. Lists, however, are designed for collections that change over time, supporting updates, insertions, deletions, and reordering. Index assignment is fundamental for many algorithms: updating an array-like buffer, modifying a dataset row, replacing incorrect values, or implementing in-place transformations efficiently.


問題 #41
What will be the result of performing the slice fam[:3]?

  • A. A list with the first four elements of fam
  • B. A list with the last three elements of fam
  • C. A list with the first three elements of fam
  • D. A list with the first two elements of fam

答案：C

解題說明：
Python slicing uses the notation sequence[start:stop], where start is inclusive and stop is exclusive. When start is omitted, it defaults to 0, meaning the slice starts from the beginning of the sequence. Therefore, fam[:3] is equivalent to fam[0:3]. Because the stop index 3 is excluded, the slice includes elements at indices 0, 1, and
2-exactly the first three elements.
This convention is emphasized in programming textbooks because it makes many tasks natural and reduces boundary errors. For example, "take the first n items" is written as [:n], and "drop the first n items" is written as [n:]. The length of the slice is also easy to reason about: with step 1, it is stop - start, so here it is 3 - 0 = 3.
Option B is incorrect because including four elements would require fam[:4]. Option C would correspond to fam[:2]. Option D describes taking elements from the end, which would use negative indexing such as fam
[-3:].
Slicing is widely used for batching, windowing in algorithms, splitting datasets into training/testing segments, and extracting prefixes in parsing tasks. Understanding the inclusive start and exclusive stop rule is essential for correct Python programming.


問題 #42
Which order is impossible when traversing a binary tree using depth first search?

  • A. Level-order traversal
  • B. Pre-order traversal
  • C. Post-order traversal
  • D. In-order traversal

答案：A

解題說明：
Depth-first search (DFS) explores a tree by going as deep as possible along a branch before backtracking. In binary trees, DFS gives rise to the classic traversal orderspre-order,in-order, andpost-order, each defined by when you "visit" the node relative to its left and right subtrees. Pre-order visits the node first, then left subtree, then right subtree. In-order visits left subtree, then the node, then right subtree. Post-order visits left subtree, then right subtree, then the node. These are all DFS-based because they fully explore subtrees before moving sideways to another branch.
Level-order traversalis different: it visits nodes layer by layer from the root outward (all nodes at depth 0, then depth 1, then depth 2, etc.). This is a hallmark ofbreadth-first search (BFS), not DFS. Textbooks emphasize this distinction because DFS and BFS have different properties: BFS naturally finds shortest paths in unweighted graphs and produces level-order traversal in trees, while DFS is useful for tasks like topological sorting, cycle detection, and exploring structure recursively.
Therefore, the traversal order that is impossible to produce as a depth-first traversal of a binary tree is level-order traversal. The DFS orders (pre-, in-, post-) are all achievable by depth-first strategies, typically implemented recursively or with an explicit stack.


問題 #43
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