DOWNLOAD the newest Exam4Labs Foundations-of-Computer-Science PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=16TeOibyuCgGldZbY_Ej4sSNHmlRVW-du
Maybe you are busy with working every day without the help of our Foundations-of-Computer-Science learning materials. The heavy work leaves you with no time to attend to study. It doesn't matter. Our Foundations-of-Computer-Science learning materials can help you squeeze your time out and allow you to improve your knowledge and skills while having work experience. And there are three versions of our Foundations-of-Computer-Science Exam Questions for you to choose according to your interests and hobbies.
| Section | Objectives |
|---|---|
| Operating Systems & Architecture | - System Architecture
|
| Programming Foundations | - Programming Concepts
|
| Computer Science Fundamentals | - Core CS Concepts
|
| Data & Security Basics | - Security Fundamentals
|
>> New Foundations-of-Computer-Science Exam Pass4sure <<
There are other countless advantages of the WGU Foundations of Computer Science Foundations-of-Computer-Science exam that you can avail of after passing the WGU Foundations of Computer Science exam. But keep in mind to pass the WGU Foundations of Computer Science Foundations-of-Computer-Science exam is a difficult job. You have to put in some extra effort, time, and investment then you will be confident to perform well in the final WGU Foundations of Computer Science exam. In this journey, you can get help from WGU Foundations of Computer Science Foundations-of-Computer-Science Dumps that will assist you in WGU Foundations of Computer Science exam preparation and prepare you to perform well in the final WGU Foundations of Computer Science exam.
NEW QUESTION # 18
How can someone subset the last two rows and columns of a 2D NumPy array?
Answer: B
Explanation:
NumPy slicing uses the same start/stop rules as Python sequences, and it also supports negative indices to count from the end. In a 2D array, slicing is written as array[rows, columns]. To get thelast two rows, you use
-2: in the row position, meaning "start two rows from the end and go to the end." Similarly, to get thelast two columns, you use -2: in the column position. Combining these gives array[-2:, -2:], which selects the bottom- right 2×2 subarray.
Option A, array[-2:, :], selects the last two rows butall columns, so it is not restricted to the last two columns.
Option D, array[:, -2:], selects all rows but only the last two columns. Option B, array[-1:, -1:], selects only the last row and the last column, producing a 1×1 (or 1×1 view) subarray, not a 2×2.
This kind of slicing is widely taught because it is essential for matrix operations, extracting submatrices, working with sliding windows, and manipulating image or time-series data where "take the last k observations/features" is common. Negative indexing reduces errors and makes code clearer, especially compared with computing explicit indices like array[rows-2:rows, cols-2:cols].
NEW QUESTION # 19
m = 30
n = 30
What will be the output of print(id(m), id(n)) after executing the following code?
Answer: A
Explanation:
In Python, id(x) returns the "identity" of an object, which in CPython (the most common implementation) is typically the object's memory address. When you write m = 30 and n = 30, both names may refer to thesame integer objectbecause CPython caches a range of small integer objects for efficiency. This optimization means that commonly used small integers are pre-created and reused, so repeated occurrences of the same small integer literal often point to the same object, producing identical id() values. As a result, print(id(m), id (n)) will most likely displaytwo identical numbersin standard CPython builds when 30 falls within the cached range. (Real Python) This behavior is an implementation detail, but it is widely discussed in Python education because it illustrates the difference between object identity (whether two variables reference the same object) and value equality (whether two objects have the same value). Even if id(m) and id(n) were different in some edge environment, m == n would still be True because the values are equal; id() is about identity, not value. The options "0 0" and "Error" are not consistent with how id() works for valid objects.
NEW QUESTION # 20
What is another term for the inputs into a function?
Answer: D
Explanation:
In programming, a function takes inputs, performs computation, and may return an output. The standard term for a function's inputs isarguments(also commonly discussed alongside the closely related termparameters).
Textbooks typically distinguish the two:parametersare the names listed in the function definition, while argumentsare the actual values supplied when the function is called. For example, in def f(x, y):, x and y are parameters. In the call f(3, 5), 3 and 5 are arguments. Many introductory materials use "arguments" informally to refer to the inputs overall, which matches the wording of this question.
Options A, B, and C do not fit the textbook definition. "Variables" is too broad; inputs can be literals, expressions, or variables, but the conceptual role is "arguments." "Procedures" are callable units of code (often used in some languages to mean functions without return values), not the inputs. "Outputs" refers to returned results, not what you pass in.
Understanding arguments is important because it connects to call semantics, scope, and correctness.
Different languages support positional arguments, keyword arguments, default values, and variadic arguments (e.g., *args, **kwargs in Python). This flexibility shapes API design and influences how programmers structure reusable code.
NEW QUESTION # 21
Given the following code, what is the expected output?
Answer: D
Explanation:
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.
NEW QUESTION # 22
What is the time complexity of a quicksort algorithm?
Answer: C
Explanation:
Quicksort is a divide-and-conquer sorting algorithm. It works by selecting a pivot element, partitioning the array into two subarrays (elements less than the pivot and elements greater than the pivot), and then recursively sorting those subarrays. In the average case, the partition step splits the array into roughly equal halves, so the recurrence is commonly written as (T(n) = T(n/2) + T(n/2) + O(n)), where (O(n)) is the cost of partitioning. This solves to (O(n \log n)), which is why quicksort is widely taught as an efficient general- purpose sorting method.
However, textbooks also emphasize that quicksort has a worst-case time complexity of (O(n
P.S. Free 2026 WGU Foundations-of-Computer-Science dumps are available on Google Drive shared by Exam4Labs: https://drive.google.com/open?id=16TeOibyuCgGldZbY_Ej4sSNHmlRVW-du