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| Section | Objectives |
|---|---|
| Topic 1: Data Profiling | - Apply fundamental concepts and subsetting techniques to a dataset - Utilize a programming language to manipulate arrays and discover insights |
| Topic 2: Algorithm Efficiency | - Choose an appropriate sorting algorithm method based on a given scenario - Choose an appropriate algorithm searching method based on a given scenario - Describe the relationships between algorithm complexity and data structures |
| Topic 3: Basic Program Design | - Identify variables and data types within a programming language - Explain how to store, access, and manipulate data in lists - Use functions, methods, and packages to leverage programming language |
| Topic 4: OS Fundamentals | - Demonstrate various techniques and tools to manage operating systems - Describe fundamental principles and core concepts of operating systems - Identify common privacy and security concepts that could be implemented in operating systems |
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15. Frage
Which principle can be used to implement an algorithm to calculate factorial or Fibonacci sequence?
Antwort: A
Begründung:
Factorial and Fibonacci are classic examples used to teachrecursion, a technique where a function solves a problem by calling itself on smaller subproblems. The key requirement for recursion is (1) abase casethat stops further calls and (2) arecursive casethat reduces the problem size. For factorial, the definition is (n! = n
\times (n-1)!) with base case (0! = 1) (or (1! = 1)). For Fibonacci, (F(n) = F(n-1) + F(n-2)) with base cases (F (0)=0) and (F(1)=1). These mathematical definitions map directly into recursive code, which is why textbooks frequently introduce recursion using these sequences.
While factorial and Fibonacci can also be computed iteratively, the question asks for the principle that can be used to implement such algorithms, and recursion is the canonical textbook answer. Recursion also connects to important CS topics: call stacks, activation records, and divide-and-conquer problem solving.
Option A ("procedural programming") and option D ("object-oriented programming") are broader paradigms rather than the specific technique used in the classic implementations. Option B ("iterative programming") is a valid alternative approach, but the standard instructional principle highlighted for these particular examples is recursion. Textbooks also note that naive recursive Fibonacci is inefficient (exponential time) unless optimized with memoization or converted to an iterative or dynamic programming approach.
16. Frage
Which file system is commonly used in Windows and supports file permissions?
Antwort: D
Begründung:
Windows commonly uses the NTFS (New Technology File System) for internal drives and many external drives because it supports advanced features required for modern operating systems. One of the most important features is support forfile and folder permissionsvia Access Control Lists (ACLs). Permissions enable the OS to enforce security policies by controlling which users and groups can read, write, execute, modify, or delete specific resources. This is fundamental to multi-user security and is a standard topic in operating systems and security textbooks.
FAT32 is an older file system designed for simplicity and broad compatibility. It does not provide the same fine-grained permission model as NTFS, which is why it is often used for removable media where cross- platform compatibility matters more than access control. HFS+ is historically associated with Apple's macOS systems, and EXT4 is widely used on Linux. While these file systems have their own permission and feature models, they are not the common Windows default for permission-managed storage in typical Windows deployments.
NTFS also supports journaling (improving reliability after crashes), large file sizes, quotas, compression, and encryption features (through Windows facilities). In enterprise environments, NTFS permissions integrate with Windows authentication and directory services, enabling centralized user management. Therefore, for Windows systems requiring file permissions, NTFS is the correct answer.
17. Frage
Which order is impossible when traversing a binary tree using depth first search?
Antwort: D
Begründung:
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.
18. Frage
How can a user subset a NumPy array bmi to only include values over 23?
Antwort: B
Begründung:
NumPy supports a powerful technique calledBoolean indexing(also called Boolean masking) to filter arrays based on a condition. When you write bmi > 23, NumPy performs an element-wise comparison and produces a Boolean array of the same shape, containing True where the condition holds and False otherwise. Using that Boolean array inside square brackets, as in bmi[bmi > 23], tells NumPy to return a new 1D array containing only the elements whose mask value is True. This approach is heavily emphasized in scientific computing curricula because it expresses selection logic without explicit loops and runs efficiently in optimized compiled code.
Option B looks close but is not standard NumPy usage. The function commonly used is np.where(condition) or np.where(condition, x, y). While np.where(bmi > 23) can return indices, bmi.where(...) is not a NumPy array method; it is more associated with pandas objects. Options A and C are not valid NumPy APIs for filtering.
Boolean indexing is central in data analysis tasks such as removing invalid measurements, selecting a population subgroup, applying thresholds, and building feature subsets. It composes cleanly with vectorized computation, for example bmi[bmi > 23].mean(), enabling concise and high-performance numerical workflows.
19. Frage
Which brand of Type 1 hypervisor is commonly used to create virtual machines?
Antwort: B
Begründung:
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.
20. Frage
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