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| Section | Objectives |
|---|---|
| Topic 1: Computer Science Fundamentals | - Data Structures Introduction
|
| Topic 2: Data & Security Basics | - Security Fundamentals
|
| Topic 3: Operating Systems & Architecture | - System Architecture
|
| Topic 4: Programming Foundations | - Programming Concepts
|
>> Exam Topics Foundations-of-Computer-Science Pdf <<
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NEW QUESTION # 61
Which order is impossible when traversing a binary tree using depth first search?
Answer: A
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 # 62
Which brand of Type 1 hypervisor is commonly used to create virtual machines?
Answer: A
Explanation:
AType 1 hypervisor, also called abare-metal hypervisor, runs directly on the host machine's hardware rather than on top of a general-purpose operating system. This design is widely described in virtualization textbooks because it improves performance and isolation: the hypervisor controls CPU scheduling, memory management, and I/O virtualization with minimal overhead from an intermediate OS layer. Type 1 hypervisors are therefore common in servers and data centers.
Among the options,VMware ESXiis the well-known Type 1 hypervisor product. It is installed directly onto physical server hardware and provides the virtualization layer used to run multiple virtual machines. In contrast, Parallels Desktop, VirtualBox, and VMware Workstation are typically categorized asType 2 hypervisors, meaning they run as applications on top of a host operating system like Windows, macOS, or Linux. Type 2 hypervisors are excellent for desktops, development, testing, and learning, but they generally rely on the host OS for device drivers and resource management, which can add overhead.
This distinction matters in practice: data centers favor Type 1 hypervisors for efficiency, centralized management, and robust isolation between workloads. Desktop users often choose Type 2 hypervisors for convenience and easier installation. Therefore, the commonly used Type 1 hypervisor brand listed here is VMware ESXi.
NEW QUESTION # 63
Which action is taken if the first number is the lowest value in a selection sort?
Answer: A
Explanation:
Selection sort works by maintaining a boundary between a sorted prefix and an unsorted suffix. On each pass, the algorithm finds the smallest value in the unsorted portion and places it into the first position of that unsorted portion (which is also the next position in the sorted prefix). This is usually done by swapping the element at the minimum's index with the element at the boundary index (the "first unsorted element"). That description matches option D.
If the first element of the unsorted portion is already the smallest, then the minimum's index equals the boundary index. In textbook implementations, the algorithm may still execute a swap operation, but it becomes a swap of an element with itself (a no-op), leaving the array unchanged. Many implementations include a small optimization: perform the swap only if the minimum index differs from the boundary index.
Either way, conceptually the "action taken" by selection sort is still "swap the selected minimum into the first unsorted position," which is exactly what option D states.
Options A and B are unrelated to sorting; selection sort never increases or duplicates values. Option C is incorrect because selection sort swaps the minimum with thefirstunsorted element, not the last. After the swap (or no-op), the sorted region grows by one element, and the algorithm repeats from the next boundary position.
This logic is fundamental for understanding how selection sort ensures correctness: after pass i, the smallest i+1 elements are fixed in their final positions.
NEW QUESTION # 64
What happens if you try to create a NumPy array with different types?
Answer: A
Explanation:
When NumPy constructs an ndarray, it chooses a single data type called the dtype for the entire array. This is a defining feature of NumPy arrays: unlike Python lists, which can hold mixed object types freely, a NumPy array is designed for efficient numerical computation by storing values in a uniform, contiguous representation. Therefore, if you provide mixed types at creation time, NumPy will select a dtype that can represent all provided values and will convert elements as needed.
This process is commonly described as type promotion or coercion to a common type. For example, mixing integers and floats produces a float array because floats can represent integers without loss of generality.
Mixing numbers and strings often results in a string dtype (or, in some cases, an object dtype), because numbers can be converted to their string representations. Once the dtype is chosen, the array behaves consistently under vectorized operations appropriate for that dtype.
Option B correctly summarizes this textbook behavior: the array will contain a single type, converting all elements to that type. Option A is too absolute-many mixed-type arrays still support calculations depending on the resulting dtype. Option C is vague and misses the crucial fact that conversion occurs. Option D is not how NumPy works; it never automatically splits inputs into multiple arrays by type.
Understanding dtype coercion matters because it affects memory usage, performance, and whether numerical operations behave as expected.
NEW QUESTION # 65
Which character is used to indicate a range of values to be sliced into a new list?
Answer: C
Explanation:
In Python, slicing is the standard mechanism for extracting arangeof elements from a sequence type such as a list, string, or tuple. The character that signals a slice range is thecolon:. The general slice syntax is sequence
[start:stop:step]. Most commonly, you see sequence[start:stop], where start is the index to begin from (inclusive) and stop is the index to end at (exclusive). This "inclusive start, exclusive stop" rule is emphasized in textbooks because it makes slice lengths easy to reason about: when step is 1, the number of elements returned is stop - start.
For example, if items = ["a", "b", "c", "d", "e"], then items[1:4] returns ["b", "c", "d"]. Omitting start defaults to the beginning (items[:3] gives the first three elements), and omitting stop defaults to the end (items[2:] gives everything from index 2 onward). The optional step supports patterns like items[::2] for every other element, and negative steps can reverse a sequence (items[::-1]).
The other characters do not define ranges in Python slicing: , separates items (or indices in multidimensional structures), + is addition/concatenation, and = is assignment. The colon is the slicing operator that indicates a range.
NEW QUESTION # 66
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