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| Section | Objectives |
|---|---|
| Computer Science Fundamentals | - Data Structures Introduction
|
| Operating Systems & Architecture | - System Architecture
|
| Programming Foundations | - Language Concepts Overview
|
| Data & Security Basics | - Security Fundamentals
|
>> Foundations-of-Computer-Science Valid Test Topics <<
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NEW QUESTION # 45
Which principle can be used to implement an algorithm to calculate factorial or Fibonacci sequence?
Answer: D
Explanation:
Factorial and Fibonacci are classic examples used to teachrecursion, a technique where a function solves a problem by calling itself on smaller subproblems. The key requirement for recursion is (1) abase casethat stops further calls and (2) arecursive casethat reduces the problem size. For factorial, the definition is (n! = n
\times (n-1)!) with base case (0! = 1) (or (1! = 1)). For Fibonacci, (F(n) = F(n-1) + F(n-2)) with base cases (F (0)=0) and (F(1)=1). These mathematical definitions map directly into recursive code, which is why textbooks frequently introduce recursion using these sequences.
While factorial and Fibonacci can also be computed iteratively, the question asks for the principle that can be used to implement such algorithms, and recursion is the canonical textbook answer. Recursion also connects to important CS topics: call stacks, activation records, and divide-and-conquer problem solving.
Option A ("procedural programming") and option D ("object-oriented programming") are broader paradigms rather than the specific technique used in the classic implementations. Option B ("iterative programming") is a valid alternative approach, but the standard instructional principle highlighted for these particular examples is recursion. Textbooks also note that naive recursive Fibonacci is inefficient (exponential time) unless optimized with memoization or converted to an iterative or dynamic programming approach.
NEW QUESTION # 46
What is the method for changing an element in a Python list?
Answer: A
Explanation:
In Python, a list is a mutable sequence, meaning its elements can be changed after the list is created. The standard textbook method for updating a specific element isindex assignment, which uses square brackets to select the position and the equals sign to assign a new value. For example, if nums = [10, 20, 30], then nums
[1] = 99 changes the element at index 1 from 20 to 99, producing [10, 99, 30]. This works because lists store references to objects and allow those references to be updated in-place.
Option B is incorrect because parentheses are used for function calls and tuples, and the plus sign typically performs concatenation (creating a new list) rather than modifying an existing element by position. Option C is incorrect because curly brackets denote dictionaries or sets, not lists. Option D is incorrect because del removes elements by index or slice (for example, del nums[1]), and it does not delete by "the element's value" unless you first find the index. Deleting is not the same as changing; deletion reduces the list's length and shifts later indices.
Index assignment is fundamental in list manipulation and appears in standard algorithms: updating counters, replacing sentinel values, editing collections, and implementing in-place transformations efficiently without allocating a new list.
NEW QUESTION # 47
Which file system is commonly used in Windows and supports file permissions?
Answer: A
Explanation:
Windows commonly uses the NTFS (New Technology File System) for internal drives and many external drives because it supports advanced features required for modern operating systems. One of the most important features is support forfile and folder permissionsvia Access Control Lists (ACLs). Permissions enable the OS to enforce security policies by controlling which users and groups can read, write, execute, modify, or delete specific resources. This is fundamental to multi-user security and is a standard topic in operating systems and security textbooks.
FAT32 is an older file system designed for simplicity and broad compatibility. It does not provide the same fine-grained permission model as NTFS, which is why it is often used for removable media where cross- platform compatibility matters more than access control. HFS+ is historically associated with Apple's macOS systems, and EXT4 is widely used on Linux. While these file systems have their own permission and feature models, they are not the common Windows default for permission-managed storage in typical Windows deployments.
NTFS also supports journaling (improving reliability after crashes), large file sizes, quotas, compression, and encryption features (through Windows facilities). In enterprise environments, NTFS permissions integrate with Windows authentication and directory services, enabling centralized user management. Therefore, for Windows systems requiring file permissions, NTFS is the correct answer.
NEW QUESTION # 48
What is a key advantage of using NumPy when handling large datasets?
Answer: C
Explanation:
NumPy's key advantage for large datasets isefficient storage and fast computation. Unlike Python lists, which store references to objects and can have per-element overhead, NumPy arrays store data in a compact, homogeneous format (single dtype) in contiguous or strided memory. This reduces memory usage and improves cache locality, which is crucial for performance on large arrays. Additionally, NumPy operations are vectorized: many computations run in optimized compiled code rather than interpreted Python loops. This enables large speedups for arithmetic, linear algebra, statistics, and transformations over entire arrays.
Option A is incorrect because NumPy itself does not provide full machine learning algorithms; those are typically found in libraries like scikit-learn, though they build on NumPy. Option B is incorrect because NumPy does not automatically clean data; data cleaning is usually done with pandas or custom logic. Option D is incorrect because interactive visualizations are typically handled by libraries like matplotlib, seaborn, or plotly, not by NumPy.
Textbooks in scientific computing highlight that NumPy forms the computational foundation of the Python data ecosystem. Its array model supports broadcasting, slicing, and efficient aggregations, all of which are essential when working with millions of numeric values. By combining compact memory layout with compiled numerical kernels, NumPy enables scalable analysis and simulation workloads that would be slow or memory-heavy using pure Python lists.
NEW QUESTION # 49
print(20 # 5)
What will the output be of this line?
Answer: C
Explanation:
In Python, the # character begins acomment. Everything from # to the end of the line is ignored by the interpreter and is not executed. Therefore, the line # print(20 # 5) producesno outputbecause it is a comment, not an executable statement. This is a standard concept in programming language textbooks: comments are for humans, not for the machine, and they are used to document code, explain intent, temporarily disable statements during debugging, or leave notes about assumptions and design choices.
Even though the line contains an unusual symbol #, it does not matter here, because the interpreter never tries to parse the commented text. If the # were removed, then Python would attempt to parse print(20 # 5), and since # is not a valid Python operator, that would indeed trigger a syntax error. But with the leading #, the entire line is inert.
Option A is incorrect because nothing is evaluated. Option C is incorrect because comments are not printed; they remain only in the source code. Option D is incorrect for the commented version of the line, since Python does not check comment contents for syntax. Thus, the correct result is no output.
NEW QUESTION # 50
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