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| Section | Weight | Objectives |
|---|---|---|
| Computer Architecture & Organization | 15% | - Instruction sets and execution cycles - Von Neumann architecture - CPU, memory, I/O systems - Memory hierarchy and performance |
| Software Engineering & Programming Basics | 15% | - Software development lifecycle - Programming paradigms - Basic syntax and control structures - Testing and debugging fundamentals |
| Data Structures | 20% | - Trees, graphs, hash tables - Primitive and composite data types - Data storage and retrieval principles - Arrays, linked lists, stacks, queues |
| Discrete Mathematics & Logic | 25% | - Propositional and predicate logic - Set theory, relations, functions - Proof techniques and mathematical induction - Boolean algebra and digital logic |
| Algorithms & Complexity | 25% | - Recursion and iterative structures - Sorting and searching algorithms - Algorithm design and analysis - Big O notation, time and space complexity |
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NEW QUESTION # 59
Which line of code below contains an error in the use of NumPy?
Answer: D
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 # 60
Which statement describes the data type restriction found in most NumPy arrays?
Answer: D
Explanation:
Most NumPy arrays enforce a key constraint: all elements share the samedtype(data type). This uniform typing is foundational to NumPy's performance model. Because each element has the same size and representation, NumPy can store the array in a contiguous memory block and apply low-level, vectorized operations efficiently. This is why NumPy is widely used for numerical computing, statistics, and data analysis: operations like addition, multiplication, and reductions (sum/mean) can be implemented in optimized compiled code without per-element Python overhead.
Option B captures this textbook principle: elements in a typical ndarray are of the same data type. The other options are incorrect. NumPy is not restricted to strings (A), and it is not limited to integers (C); it supports floats, complex numbers, booleans, fixed-width strings, datetime types, and many others. Option D is misleading: NumPy does not continuously "adapt on the fly" during normal use. The dtype is generally fixed once the array exists. What NumPydoesdo is choose an appropriate common dtype when you create an array from mixed inputs (for example, mixing ints and floats yields floats). But after creation, assignments are cast into the existing dtype rather than dynamically changing the dtype to accommodate new values.
This restriction is precisely what differentiates NumPy arrays from Python lists and enables predictable memory layout and fast numerical computation.
NEW QUESTION # 61
What code would print a subarray of the first 5 elements in numpy_array?
Answer: D
Explanation:
NumPy arrays support slicing using the same start:stop convention as Python sequences. To take the first five elements, you want indices 0 through 4. The slice numpy_array[:5] means "start from the beginning (default start is 0) and stop before index 5." Because the stop index is exclusive, this returns exactly the first five elements. Printing that slice with print(numpy_array[:5]) displays a 1D view (or copy depending on context) containing those elements.
Option A, numpy_array[1:5], starts at index 1, so it returns elements 1 through 4-only four elements-and it excludes the element at index 0, so it is not the first five elements. Options B and D are incorrect because NumPy arrays do not provide a .get() method for slicing in this manner; .get() is a method associated with dictionaries, not arrays.
Textbooks stress slicing because it is efficient and expressive, especially in data analysis. With slicing, you can take prefixes, suffixes, windows, or regularly spaced samples without writing loops. In NumPy, slicing is particularly important because many slices create views into the same underlying data buffer, enabling memory-efficient operations on large datasets. Understanding inclusive start and exclusive stop boundaries is critical to avoid off-by-one mistakes and to work correctly with batches and segments of numerical data.
NEW QUESTION # 62
Which aspect of a security policy would define the ramifications of abusing company resources?
Answer: C
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 # 63
Which order is impossible when traversing a binary tree using depth first search?
Answer: C
Explanation:
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.
NEW QUESTION # 64
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