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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Software Engineering & Programming Basics | 15% | - Programming paradigms - Basic syntax and control structures - Testing and debugging fundamentals - Software development lifecycle |
| Topic 2: Discrete Mathematics & Logic | 25% | - Propositional and predicate logic - Set theory, relations, functions - Proof techniques and mathematical induction - Boolean algebra and digital logic |
| Topic 3: Data Structures | 20% | - Trees, graphs, hash tables - Primitive and composite data types - Data storage and retrieval principles - Arrays, linked lists, stacks, queues |
| Topic 4: Algorithms & Complexity | 25% | - Sorting and searching algorithms - Algorithm design and analysis - Recursion and iterative structures - Big O notation, time and space complexity |
| Topic 5: Computer Architecture & Organization | 15% | - Von Neumann architecture - CPU, memory, I/O systems - Memory hierarchy and performance - Instruction sets and execution cycles |
>> Foundations-of-Computer-Science인증시험 덤프문제 <<
저희는 수많은 IT자격증시험에 도전해보려 하는 IT인사들께 편리를 가져다 드리기 위해 WGU Foundations-of-Computer-Science실제시험 출제유형에 근거하여 가장 퍼펙트한 시험공부가이드를 출시하였습니다. 많은 사이트에서 판매하고 있는 시험자료보다 출중한ITDumpsKR의 WGU Foundations-of-Computer-Science덤프는 실제시험의 거의 모든 문제를 적중하여 고득점으로 시험에서 한방에 패스하도록 해드립니다. WGU Foundations-of-Computer-Science시험은ITDumpsKR제품으로 간편하게 도전해보시면 후회없을 것입니다.
질문 # 24
Which order is impossible when traversing a binary tree using depth first search?
정답：B
설명：
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.
질문 # 25
Which type of data structure is the only focus of a binary search?
정답：B
설명：
Binary search is designed for searching in asorted (ordered) sequence. Its efficiency comes from repeatedly comparing the target to the middle element and discarding half of the remaining search space. This halving logic only works when the data is ordered, because the algorithm relies on the guarantee that all elements on one side of the midpoint are smaller (or larger) than the midpoint. In textbooks, this requirement is stated explicitly: binary search assumes the collection is sorted according to the same ordering used for comparisons.
An "ordered list" is therefore the correct focus among the options. Binary search can be implemented on arrays or other random-access structures where you can quickly access the middle element by index. While you can conceptually perform binary search on a linked list, it becomes inefficient because finding the middle requires linear traversal, losing the O(log n) advantage. Stacks and queues are not appropriate because they restrict access to ends only (LIFO for stacks, FIFO for queues), preventing direct access to the midpoint and making the binary search strategy infeasible.
Thus, the central requirement for binary search is a sorted/ordered sequence, typically supporting efficient indexing, which is why the correct choice is an ordered list.
질문 # 26
What statistical measure can be used to detect outliers in a dataset using NumPy?
정답：C
설명：
Outlier detection often relies on measuring how far values deviate from a "typical" center. While variance and standard deviation can be used in simple z-score based methods, they arenot robust: a few extreme outliers can inflate the mean and standard deviation, masking the very outliers you want to find. A widely taught robust alternative is themedian absolute deviation (MAD), which is based on the median rather than the mean and therefore resists distortion by extreme values.
MAD is computed by first taking the median of the data, then computing the absolute deviation of each point from that median, and finally taking the median of those deviations. Because medians are stable under extreme values, MAD provides a strong baseline for identifying unusually distant points. Many textbooks and data analysis references present MAD as a robust scale estimator for outlier detection, often combined with a threshold rule such as flagging points whose deviation exceeds a constant multiple of MAD (with a scaling factor sometimes used to make it comparable to standard deviation under normality assumptions).
In NumPy, you can implement MAD using np.median() and np.abs(). Mode is generally not useful for continuous numeric outlier detection, and variance/standard deviation are more sensitive to outliers than MAD. Thus, among the given options, the best statistical measure for detecting outliers robustly is the median absolute deviation.
질문 # 27
What is the layer of programming between the operating system and the hardware that allows the operating system to interact with it in a more independent and generalized manner?
정답：D
설명：
TheHardware Abstraction Layer (HAL)is a software layer that sits between the operating system kernel and the physical hardware. Its purpose is to hide hardware-specific details behind a consistent interface, allowing the OS to be more portable and easier to maintain across different hardware platforms. Textbooks explain that without abstraction, the OS would need extensive device- and architecture-specific code scattered throughout the kernel, making updates and cross-platform support far more difficult.
The HAL typically provides standardized functions for interacting with low-level components such as interrupts, timers, memory mapping, and device I/O. With a HAL, the OS can call general routines (for example, to configure an interrupt controller) while the HAL handles the platform-specific implementation.
This supports a key systems principle: separate policy (what the OS wants to do) from mechanism (how hardware accomplishes it).
The other options are not correct. A boot loader runs at startup to load the operating system into memory; it is not the general interface layer during normal operation. The task scheduler is a kernel subsystem that manages CPU time among processes, not a hardware-independence layer. The file system layer manages storage organization and access semantics; it is not the general abstraction for all hardware interactions.
Therefore, the programming layer that enables generalized OS interaction with hardware is the hardware abstraction layer.
질문 # 28
What is an ndarray in Python?
정답：D
설명：
An ndarray is NumPy's fundamental data structure: ann-dimensional arraydesigned for efficient numerical computation. The term stands for "N-dimensional array," and it is implemented as numpy.ndarray. Unlike Python's built-in list, an ndarray stores elements in a compact, homogeneous format defined by its dtype (such as integers or floating-point numbers). This uniform representation enables fast, vectorized operations and efficient use of memory, which is why ndarray is central in scientific computing and data analysis.
An ndarray supports multiple dimensions: a 1D array behaves like a vector, a 2D array like a matrix (rows and columns), and higher-dimensional arrays represent tensors. Textbooks emphasize that ndarray operations are typically element-wise by default (for example, a + b adds corresponding elements), and that slicing and broadcasting allow powerful computations without explicit loops. This approach is both expressive and efficient because the heavy lifting happens in optimized low-level code.
Option A is incorrect because ndarray is not built into core Python; it comes from NumPy. Option B describes a tree, which is a different data structure entirely. Option D is incorrect because sockets and XML-related functionality belong to other parts of Python's standard library, not to NumPy or ndarray.
In short, an ndarray is the primary array object of NumPy, providing high-performance multi- dimensional numerical storage and computation.
질문 # 29
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