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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Software Engineering & Programming Basics | 15% | - Basic syntax and control structures - Programming paradigms - Testing and debugging fundamentals - Software development lifecycle |
| Topic 2: Computer Architecture & Organization | 15% | - CPU, memory, I/O systems - Instruction sets and execution cycles - Memory hierarchy and performance - Von Neumann architecture |
| Topic 3: Data Structures | 20% | - Data storage and retrieval principles - Primitive and composite data types - Arrays, linked lists, stacks, queues - Trees, graphs, hash tables |
| Topic 4: Discrete Mathematics & Logic | 25% | - Set theory, relations, functions - Boolean algebra and digital logic - Propositional and predicate logic - Proof techniques and mathematical induction |
| Topic 5: Algorithms & Complexity | 25% | - Recursion and iterative structures - Sorting and searching algorithms - Algorithm design and analysis - Big O notation, time and space complexity |
>> Foundations-of-Computer-Science Exam Topics <<
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NEW QUESTION # 26
Which line of code below contains an error in the use of NumPy?
Answer: C
Explanation:
The NumPy library provides arrays and efficient numerical operations, including sorting. However, NumPy doesnotprovide a function named np.quicksort. That is the API misuse in the code, making option A the correct answer. In NumPy, sorting is commonly performed using np.sort(arr) (which returns a sorted copy) or arr.sort() (which sorts in-place). If a specific algorithm is desired, NumPy exposes it through the kind parameter, such as np.sort(arr, kind="quicksort"), kind="mergesort", or kind="heapsort". Textbooks present this as a typical design: a single sorting interface with selectable strategies, rather than separate top-level functions per algorithm name.
Option C is correct and necessary: import numpy as np is standard convention. Option B is also correct:
printing a variable is valid assuming it exists. Option D, written as arr = np.array([3, 2, 0, 1]), is valid NumPy usage for constructing a 1D array from a Python list.
A subtle point taught in scientific computing courses is that library APIs matter as much as syntax: you can write perfectly valid Python that still fails if you call a function that the library does not define. In this case, the fix is to replace np.quicksort(arr) with np.sort(arr) or np.sort(arr, kind="quicksort") depending on whether you need to specify the algorithm.
NEW QUESTION # 27
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 # 28
What is the purpose of user management and access control in a networked environment?
Answer: B
Explanation:
In a networked environment, user management and access control exist to ensure that resources are used securely, appropriately, and accountably. The core idea isauthorization: defining what each user (or group of users) is allowed to do-read files, modify data, access applications, administer systems, and so on. This is commonly guided by the principle ofleast privilege, which states that users should receive only the permissions necessary to perform their tasks. Proper access control reduces the damage from mistakes and limits the impact of compromised accounts.
User management also includesauthenticationsupport (ensuring a user is who they claim to be) and administrative functions such as creating accounts, assigning roles, revoking access, and enforcing policies (password rules, multi-factor authentication requirements, session timeouts). In many systems, access control is implemented through models like discretionary access control (DAC), role-based access control (RBAC), or mandatory access control (MAC), each with different security properties.
Option B correctly reflects this: the goal is to establish permissions and to monitor or audit usage (logging access, tracking changes, detecting suspicious behavior). Option A is wrong because equal access is rarely secure or practical. Option C is the opposite of secure practice. Option D is too absolute:
systems typically restrict some users from some confidential resources, not all users from all confidential documents.
NEW QUESTION # 29
Which aspect of a security policy would define the ramifications of abusing company resources?
Answer: D
Explanation:
AnAcceptable Use Policy (AUP)defines how employees and users are permitted to use an organization's computing resources-such as email, internet access, file storage, endpoints, and networks-and it typically specifies prohibited behaviors and the consequences of violations. In security and IT governance textbooks, the AUP is framed as both a behavioral contract and a risk-management tool: it reduces misuse, clarifies expectations, and provides an enforceable basis for disciplinary action.
The "ramifications of abusing company resources" (for example, installing unauthorized software, excessive personal use, accessing inappropriate content, attempting to bypass security controls, or sharing credentials) are precisely the kinds of issues an AUP addresses. The policy often includes monitoring statements (users have limited expectation of privacy), compliance requirements, and escalation paths for violations.
A Network Security Policy (A) focuses on technical rules for network protection-firewalls, segmentation, remote access, and intrusion detection-rather than broad user conduct and disciplinary consequences. A Physical Security Policy (B) addresses protection of facilities and hardware-badges, locks, visitor procedures, secure areas. A Data Retention Policy (D) defines how long data is stored, how it is archived, and how it is disposed, which is different from defining misuse consequences.
Thus, the policy aspect that defines permissible behavior and the consequences for abusing resources is the Acceptable Use Policy.
NEW QUESTION # 30
What will the expression fam[3:6] return?
Answer: D
Explanation:
Python slicing follows the rule `sequence[start:stop]`, where the `start` index is **inclusive** and the `stop` index is **exclusive**. This convention is taught widely because it makes many algorithms and boundary cases simpler: the length of the slice is `stop - start` (when step is 1), and adjacent slices can partition a sequence without overlap. For a list named `fam`, the slice `fam[3:6]` starts at index 3 and includes the elements at indices 3, 4, and 5, but it stops before index 6.
This is a frequent source of off-by-one errors for beginners, so textbooks emphasize remembering: "start is included, stop is not." If `fam` had at least 6 elements, then `fam[3:6]` would produce a new list of exactly three elements (positions 3, 4, 5). If `fam` had fewer than 6 elements, Python would still return a valid slice up to the end without raising an error, because slicing is designed to be safe within bounds.
# Option A is incorrect because it skips index 3 and incorrectly includes index 6. Option B is incorrect because it includes index 6, which the stop boundary excludes. Option D is incorrect because slicing returns a sublist, not a single element; a single element would require indexing like `fam[6]`.
NEW QUESTION # 31
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