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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Computer Architecture & Organization | 15% | - Memory hierarchy and performance - Instruction sets and execution cycles - Von Neumann architecture - CPU, memory, I/O systems |
| Topic 2: Algorithms & Complexity | 25% | - Recursion and iterative structures - Algorithm design and analysis - Sorting and searching algorithms - Big O notation, time and space complexity |
| Topic 3: Data Structures | 20% | - Data storage and retrieval principles - Primitive and composite data types - Trees, graphs, hash tables - Arrays, linked lists, stacks, queues |
| 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: Software Engineering & Programming Basics | 15% | - Programming paradigms - Basic syntax and control structures - Testing and debugging fundamentals - Software development lifecycle |
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NEW QUESTION # 49
What is the slicing outcome of client_locations[1:3] from client_locations = ["TX", "AZ", "UT", "NY"]?
Answer: D
Explanation:
Python list slicing uses the notation list[start:stop], where start is inclusive and stop is exclusive. This means the slice begins at index start and includes elements up to, but not including, index stop. Lists in Python are zero-indexed, so for client_locations = ["TX", "AZ", "UT", "NY"], the indices are: 0 # "TX", 1 # "AZ", 2 #
"UT", 3 # "NY".
The slice client_locations[1:3] starts at index 1 and stops before index 3. Therefore, it includes elements at indices 1 and 2, which are "AZ" and "UT". The result is ["AZ", "UT"].
This slice rule is heavily emphasized in programming textbooks because it supports efficient sub-list extraction and is consistent across Python sequence types such as strings and tuples. It also helps avoid off-by-one errors by using an exclusive end boundary. The exclusive stop index makes it easy to take
"the first n items" via [0:n] and to split sequences at a boundary without overlap. In practical software development, slicing is widely used for batching data, windowing in algorithms, and parsing structured inputs, making it an essential Python skill.
NEW QUESTION # 50
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 # 51
What is the expected output of calling .shape on a NumPy 2D array?
Answer: A
Explanation:
In NumPy, every ndarray has a shape attribute that describes the size of the array along each dimension. For a
2D array, shape returns a tuple with two integers: (number_of_rows, number_of_columns). For example, if a
= np.array([[1, 2, 3], [4, 5, 6]]), then a.shape is (2, 3), meaning 2 rows and 3 columns. This is a fundamental idea in matrix and array computing, because shape governs how indexing, slicing, broadcasting, and linear algebra operations behave.
Option A describes the dtype, which can be accessed with a.dtype, not a.shape. Option C is incorrect because shape provides per-dimension sizes, not their sum. Option D refers to the total number of elements, which NumPy provides via a.size (or equivalently np.prod(a.shape)).
Textbooks emphasize shape because many errors in numerical computing come from mismatched dimensions. For example, matrix multiplication requires compatible inner dimensions, and broadcasting rules depend on dimension sizes. By checking .shape, programmers can verify their data layout before applying algorithms, ensuring rows represent observations and columns represent features (or vice versa). Thus, for a 2D NumPy array, .shape indicates the number of rows and columns.
NEW QUESTION # 52
Which statement describes the relationship between trees and graphs?
Answer: A
Explanation:
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)
NEW QUESTION # 53
Which Windows 11 tool enables a user to manually add a Bluetooth device if it does not automatically configure when first connected?
Answer: B
Explanation:
When a Bluetooth device does not configure automatically, the underlying issue is often driver discovery, device enumeration, or the Bluetooth adapter's state. In Windows, the tool traditionally associated with manually managing hardware devices and their drivers isDevice Manager. It lets a user view hardware categories (including Bluetooth adapters), enable or disable devices, update drivers, uninstall and rescan, and address "unknown device" situations. These actions are core to manual configuration because they influence whether Windows can properly recognize and communicate with a Bluetooth device.
Windows 11 pairing itself is typically initiated from the Settings app under Bluetooth and devices, where a user chooses "Add device" to pair a new accessory. (Microsoft Support) However, among the options provided, only Device Manager is a hardware-configuration tool that can resolve situations where automatic configuration fails due to driver or adapter problems. Network-related tools do not handle local device drivers, Task Scheduler automates tasks rather than adding devices, and Windows Defender is focused on security and malware protection rather than device setup.
From a systems perspective, this reflects a key operating-systems concept: successful device use requires both discovery/pairing and a correctly installed driver stack. Device Manager is the standard interface for the driver and device side of that equation, which is why it is the best match to "manually add or configure" hardware in the given choices.
NEW QUESTION # 54
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