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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Algorithms & Complexity | 25% | - Big O notation, time and space complexity - Recursion and iterative structures - Sorting and searching algorithms - Algorithm design and analysis |
| Topic 2: Computer Architecture & Organization | 15% | - Von Neumann architecture - Instruction sets and execution cycles - CPU, memory, I/O systems - Memory hierarchy and performance |
| Topic 3: Software Engineering & Programming Basics | 15% | - Testing and debugging fundamentals - Software development lifecycle - Basic syntax and control structures - Programming paradigms |
| Topic 4: Discrete Mathematics & Logic | 25% | - Boolean algebra and digital logic - Propositional and predicate logic - Proof techniques and mathematical induction - Set theory, relations, functions |
| Topic 5: Data Structures | 20% | - Data storage and retrieval principles - Arrays, linked lists, stacks, queues - Trees, graphs, hash tables - Primitive and composite data types |
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71. Frage
What is the correct way to represent a boolean value in Python?
Antwort: C
Begründung:
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.
72. Frage
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?
Antwort: D
Begründung:
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."
73. Frage
Which is the most powerful command line interface on Windows systems?
Antwort: A
Begründung:
On Windows,PowerShellis generally regarded as the most powerful command-line environment because it is both a shell and a scripting language designed for system administration and automation. Traditional Command Promptfocuses on running console commands and batch files with plain-text input and output.
PowerShell, by contrast, uses an object-oriented pipeline: commands (calledcmdlets) output structured objects rather than raw text. This enables more reliable scripting and data manipulation, since you can filter, sort, and transform results without fragile text parsing.
Textbooks covering operating systems and administration emphasize automation and management at scale.
PowerShell integrates tightly with Windows management technologies, such as WMI/CIM, the registry, services, event logs, and Active Directory environments. It also supports remote management, scripting modules, robust error handling, and modern security features. This makes it particularly suitable for tasks like provisioning users, configuring machines, auditing systems, and orchestrating deployments.
The other options are not command-line interfaces in the same sense. Task Manager is a GUI tool for viewing processes and performance. Control Panel is also GUI-based for system configuration. Command Prompt is a command line interface, but it is less capable for complex administration compared to PowerShell's scripting and object pipeline.
Therefore, from a computer science and systems perspective, PowerShell is the most powerful Windows CLI environment among the choices.
74. Frage
What is a key advantage of using NumPy when handling large datasets?
Antwort: C
Begründung:
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.
75. Frage
Which statement describes the relationship between trees and graphs?
Antwort: A
Begründung:
In discrete mathematics and computer science, atreeis a special kind ofgraph. The standard graph-theory definition is that a tree is aconnected, acyclicundirected graph. "Acyclic" means it containsno cycles, i.e., you cannot start at a vertex, follow a sequence of edges, and return to the starting vertex without repeating edges in a way that forms a loop. (Wikipedia) This property is exactly what makes option D correct.
The other options contradict the definition. If a structure has cycles, it is not a tree (though it may still be a graph). If it has unconnected nodes, it is not connected; such a structure is more like aforest(a disjoint union of trees) rather than a single tree. (Wikipedia) The idea of "levels" belongs to a particular computer-science representation called arooted tree, where one node is chosen as the root and nodes can be assigned depths
/levels based on distance from the root. But levels are not required in the abstract definition of a tree as a graph; they arise from choosing a root and orientation for convenience in algorithms like BFS/DFS, heaps, and parse trees.
So, the relationship is: every tree is a graph with extra structure-specifically, no cycles and (typically) connectivity-and the "no cycles" rule is the key distinguishing feature. (Discrete Mathematics)
76. Frage
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