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| Section | Weight | Objectives |
|---|---|---|
| Computer Architecture & Organization | 15% | - Von Neumann architecture - CPU, memory, I/O systems - Instruction sets and execution cycles - Memory hierarchy and performance |
| Software Engineering & Programming Basics | 15% | - Testing and debugging fundamentals - Software development lifecycle - Programming paradigms - Basic syntax and control structures |
| Algorithms & Complexity | 25% | - Sorting and searching algorithms - Big O notation, time and space complexity - Recursion and iterative structures - Algorithm design and analysis |
| Data Structures | 20% | - Trees, graphs, hash tables - Data storage and retrieval principles - Primitive and composite data types - Arrays, linked lists, stacks, queues |
| Discrete Mathematics & Logic | 25% | - Proof techniques and mathematical induction - Boolean algebra and digital logic - Propositional and predicate logic - Set theory, relations, functions |
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NEW QUESTION # 26
Which sorting algorithm works by finding the smallest or largest element in an unsorted part of a list and moving it to the sorted part of the list?
Answer: A
Explanation:
Selection sort is defined by a simple repeated strategy: divide the list into a sorted region and an unsorted region, then repeatedly select the smallest (or largest) element from the unsorted region and move it to the end of the sorted region. In the common "smallest-first" version, the algorithm scans the unsorted portion to find the minimum element, then swaps it into the next position in the sorted portion. After the first pass, the smallest element is fixed at index 0; after the second pass, the second-smallest is fixed at index 1; and so on until the entire list is sorted.
This exactly matches the description in the question, making selection sort the correct answer. Textbooks often use selection sort to teach algorithmic thinking because it is easy to understand and implement, though not efficient for large datasets. Its time complexity is O(n²) in the average and worst case because it performs roughly n scans of progressively smaller unsorted sections, with each scan taking linear time. Its space usage is O(1) additional space because it sorts in place using swaps.
The other options do not match the described mechanism. Quicksort partitions around a pivot, heap sort uses a heap data structure to repeatedly extract the maximum/minimum, and radix sort processes digits/keys by place value rather than selecting minima by scanning. Selection sort's defining action is the repeated "select the min/max and place it."
NEW QUESTION # 27
What is the correct way to represent a boolean value in Python?
Answer: D
Explanation:
Python has a built-in boolean type named bool, which has exactly two values: True and False. These are language keywords/constants and are case-sensitive. Therefore, the correct representation of a boolean value is True (capital T, lowercase rest) or False (capital F). This is consistently taught in introductory programming textbooks because it affects conditional statements (if, while), logical operations (and, or, not), and comparisons.
Option A, "True", is a string literal, not a boolean. While it visually resembles the boolean constant, it behaves differently: non-empty strings are "truthy" in conditions, but "True" == True is false because they are different types (str vs bool). Option B, "true", is also a string, and it differs in casing as well. Option D, true, is not valid in Python; it will raise a NameError unless a variable named true has been defined.
Textbooks also stress that boolean values often result from comparisons, such as x > 0, and that booleans are a subtype of integers in Python (True behaves like 1 and False like 0 in arithmetic contexts). Still, their primary use is representing logical truth values for control flow and decision- making.
NEW QUESTION # 28
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 # 29
What is a key advantage of using NumPy when handling large datasets?
Answer: B
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 # 30
What is the main advantage of using NumPy arrays over regular Python lists for data analysis?
Answer: C
Explanation:
The primary advantage of NumPy arrays in data analysis is their support for fast, vectorized computation over whole collections of numeric data. A NumPy `ndarray` stores elements in a contiguous memory block with a single, fixed data type, enabling efficient low-level operations implemented in optimized C/Fortran code. As a result, expressions like `arr + 5`, `arr * arr`, or `np.mean(arr)` operate over the entire array without explicit Python loops. This style is commonly called **vectorization**, and it is a central theme in scientific computing textbooks because it is both clearer to read and significantly faster for large datasets.
Option A describes a property of Python lists, not NumPy arrays. Python lists can mix types freely, but this flexibility comes with overhead. Option B is true-NumPy arrays typically hold a single dtype-but it is not the main advantage; it is more of an implementation feature that enables speed and memory efficiency.
Option D is not a defining advantage; both lists and arrays can be concatenated, and NumPy provides dedicated functions such as `np.concatenate`, but concatenation is not the core reason NumPy dominates data analysis workflows.
# Because NumPy operations are applied element-wise across entire arrays and can leverage CPU vector instructions and efficient memory access patterns, they form the foundation for higher-level tools like pandas, SciPy, and many machine learning libraries. This is why the best answer is that NumPy arrays can perform calculations over entire collections of values.
NEW QUESTION # 31
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