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

Section Objectives
Topic 1: 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 2: Algorithm Efficiency - Choose an appropriate algorithm searching method based on a given scenario
- Describe the relationships between algorithm complexity and data structures
- Choose an appropriate sorting algorithm method based on a given scenario
Topic 3: Data Profiling - Utilize a programming language to manipulate arrays and discover insights
- Apply fundamental concepts and subsetting techniques to a dataset
Topic 4: Basic Program Design - Identify variables and data types within a programming language
- Use functions, methods, and packages to leverage programming language
- Explain how to store, access, and manipulate data in lists

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最新的 Courses and Certificates Foundations-of-Computer-Science 免費考試真題 (Q41-Q46):

問題 #41
What is the expected output of numpy_array[1]?

  • A. The first element of the array
  • B. The second element of the array
  • C. A display of the entire array
  • D. An error message in the array

答案：B

解題說明：
In Python and NumPy, indexing iszero-based, meaning the first element of a 1D sequence is at index 0, the second element is at index 1, and so on. A NumPy array behaves like a sequence for basic indexing, so numpy_array[1] returns the element stored at position 1 in the array. This is a fundamental concept taught in introductory programming and scientific computing: indexing selects a single element, while slicing selects a range.
For example, if numpy_array = np.array([5, 8, 13]), then numpy_array[0] is 5, numpy_array[1] is 8, and numpy_array[2] is 13. The expression numpy_array[1] therefore evaluates to thesecond element(8 in this example). This does not display the entire array (that would happen with print(numpy_array)), and it does not produce an error unless the array is too short. An error such as IndexError occurs only if index 1 is out of bounds, for example when the array has length 1 and you try to access numpy_array[1].
Textbooks emphasize careful reasoning about indices because off-by-one errors are common. In data analysis, correct indexing is crucial for extracting the right observations, features, or time steps from numerical datasets.


問題 #42
Given the following code, what is the expected output?

  • A. [10, 20, 30, 40]
  • B. array([10, 20, 30, 40])
  • C. [1, 10]
  • D. [1, 2, 3, 4]

答案：D

解題說明：
In NumPy, a 2D array can be visualized as a table of rows and columns. When you write np_2d[0], you are usingzero-based indexingto select thefirst rowof that 2D array. This is a standard convention in Python and many other programming languages: index 0 refers to the first element, index 1 to the second, and so on.
Therefore, np_2d[0] returns all the elements in row 0.
With a typical construction such as np_2d = np.array([[1, 2, 3, 4], [10, 20, 30, 40]]), the first row is [1, 2, 3,
4], so printing np_2d[0] displays that row. NumPy returns the row as a 1D NumPy array, and when printed it often appears in bracket form like [1 2 3 4] (spaces rather than commas are common in NumPy's display).
Conceptually, however, the contents are exactly the first row values, matching option C.
Option A and D show the second row (index 1), not the first. Option B incorrectly suggests a column extraction rather than a row selection.


問題 #43
What is the likely cause if a default Python configuration does not recognize a NumPy array as an allowed data structure?

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

答案：D

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 #44
Which Windows 11 tool enables a user to manually add a Bluetooth device if it does not automatically configure when first connected?

  • A. Task scheduler
  • B. Windows defender
  • C. Network center
  • D. Device manager

答案：D

解題說明：
When a Bluetooth device does not configure automatically, the underlying issue is often driver discovery, device enumeration, or the Bluetooth adapter's state. In Windows, the tool traditionally associated with manually managing hardware devices and their drivers isDevice Manager. It lets a user view hardware categories (including Bluetooth adapters), enable or disable devices, update drivers, uninstall and rescan, and address "unknown device" situations. These actions are core to manual configuration because they influence whether Windows can properly recognize and communicate with a Bluetooth device.
Windows 11 pairing itself is typically initiated from the Settings app under Bluetooth and devices, where a user chooses "Add device" to pair a new accessory. (Microsoft Support) However, among the options provided, only Device Manager is a hardware-configuration tool that can resolve situations where automatic configuration fails due to driver or adapter problems. Network-related tools do not handle local device drivers, Task Scheduler automates tasks rather than adding devices, and Windows Defender is focused on security and malware protection rather than device setup.
From a systems perspective, this reflects a key operating-systems concept: successful device use requires both discovery/pairing and a correctly installed driver stack. Device Manager is the standard interface for the driver and device side of that equation, which is why it is the best match to "manually add or configure" hardware in the given choices.


問題 #45
What statistical measure can be used to detect outliers in a dataset using NumPy?

  • A. Standard deviation
  • B. Mode
  • C. Variance
  • D. Median absolute deviation

答案：D

解題說明：
Outlier detection often relies on measuring how far values deviate from a "typical" center. While variance and standard deviation can be used in simple z-score based methods, they arenot robust: a few extreme outliers can inflate the mean and standard deviation, masking the very outliers you want to find. A widely taught robust alternative is themedian absolute deviation (MAD), which is based on the median rather than the mean and therefore resists distortion by extreme values.
MAD is computed by first taking the median of the data, then computing the absolute deviation of each point from that median, and finally taking the median of those deviations. Because medians are stable under extreme values, MAD provides a strong baseline for identifying unusually distant points. Many textbooks and data analysis references present MAD as a robust scale estimator for outlier detection, often combined with a threshold rule such as flagging points whose deviation exceeds a constant multiple of MAD (with a scaling factor sometimes used to make it comparable to standard deviation under normality assumptions).
In NumPy, you can implement MAD using np.median() and np.abs(). Mode is generally not useful for continuous numeric outlier detection, and variance/standard deviation are more sensitive to outliers than MAD. Thus, among the given options, the best statistical measure for detecting outliers robustly is the median absolute deviation.


問題 #46
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