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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Discrete Mathematics & Logic | 25% | - Boolean algebra and digital logic - Set theory, relations, functions - Proof techniques and mathematical induction - Propositional and predicate logic |
| Topic 2: Software Engineering & Programming Basics | 15% | - Testing and debugging fundamentals - Software development lifecycle - Basic syntax and control structures - Programming paradigms |
| Topic 3: Algorithms & Complexity | 25% | - Algorithm design and analysis - Sorting and searching algorithms - Recursion and iterative structures - Big O notation, time and space complexity |
| Topic 4: Data Structures | 20% | - Primitive and composite data types - Arrays, linked lists, stacks, queues - Trees, graphs, hash tables - Data storage and retrieval principles |
| Topic 5: Computer Architecture & Organization | 15% | - Memory hierarchy and performance - Instruction sets and execution cycles - CPU, memory, I/O systems - Von Neumann architecture |
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NEW QUESTION # 15
Given the following code, what is the expected output?
Answer: D
Explanation:
In NumPy, a 2D array can be visualized as a table of rows and columns. When you write np_2d[0], you are usingzero-based indexingto select thefirst rowof that 2D array. This is a standard convention in Python and many other programming languages: index 0 refers to the first element, index 1 to the second, and so on.
Therefore, np_2d[0] returns all the elements in row 0.
With a typical construction such as np_2d = np.array([[1, 2, 3, 4], [10, 20, 30, 40]]), the first row is [1, 2, 3,
4], so printing np_2d[0] displays that row. NumPy returns the row as a 1D NumPy array, and when printed it often appears in bracket form like [1 2 3 4] (spaces rather than commas are common in NumPy's display).
Conceptually, however, the contents are exactly the first row values, matching option C.
Option A and D show the second row (index 1), not the first. Option B incorrectly suggests a column extraction rather than a row selection.
NEW QUESTION # 16
How can a user subset a NumPy array bmi to only include values over 23?
Answer: D
Explanation:
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.
NEW QUESTION # 17
What will be the result of performing the slice fam[:3]?
Answer: A
Explanation:
Python slicing uses the notation sequence[start:stop], where start is inclusive and stop is exclusive. When start is omitted, it defaults to 0, meaning the slice starts from the beginning of the sequence. Therefore, fam[:3] is equivalent to fam[0:3]. Because the stop index 3 is excluded, the slice includes elements at indices 0, 1, and
2-exactly the first three elements.
This convention is emphasized in programming textbooks because it makes many tasks natural and reduces boundary errors. For example, "take the first n items" is written as [:n], and "drop the first n items" is written as [n:]. The length of the slice is also easy to reason about: with step 1, it is stop - start, so here it is 3 - 0 = 3.
Option B is incorrect because including four elements would require fam[:4]. Option C would correspond to fam[:2]. Option D describes taking elements from the end, which would use negative indexing such as fam
[-3:].
Slicing is widely used for batching, windowing in algorithms, splitting datasets into training/testing segments, and extracting prefixes in parsing tasks. Understanding the inclusive start and exclusive stop rule is essential for correct Python programming.
NEW QUESTION # 18
What Python code would return the value 2 from np_2d, where np_2d = np.array([[1, 2, 3, 4], [10, 20, 30,
40]])?
Answer: B
Explanation:
NumPy arrays support multi-dimensional indexing using a comma-separated index tuple. For a 2D array, the first index selects the row and the second index selects the column. With np_2d = np.array([[1, 2, 3, 4], [10,
20, 30, 40]]), row 0 is [1, 2, 3, 4]. Within that row, column 1 is the second element, which is 2. Therefore, np_2d[0, 1] returns 2.
Option A is incorrect because np_2d[0,1] already produces a scalar (an integer), and indexing a scalar again with [1] is invalid. Option C, np_2d[2], attempts to access the third row, but this array has only two rows (indices 0 and 1), so it would raise an index error. Option D, np_2d[2, 0], also references a non-existent third row and would error.
This indexing rule is foundational in array-based computing: it provides direct access to elements without loops and supports efficient numerical computation. Understanding row/column indexing is essential for slicing, broadcasting, and matrix operations taught in scientific computing curricula.
NEW QUESTION # 19
Which type of sorting algorithm starts at the first position and moves the pointer until the end of the list, determining the lowest value?
Answer: D
Explanation:
Selection sort is the algorithm that repeatedly scans the unsorted portion of a list to find the lowest (or highest) value and then places it into its correct position in the sorted portion. It begins at the first index (position 0) and treats that as the boundary between sorted and unsorted regions. On the first pass, it moves a scanning pointer through the entire list to determine the minimum element and swaps it into position 0. On the second pass, it starts from position 1, scans to the end to find the next minimum, and swaps it into position 1.
This continues until the list is sorted.
This matches the question's description: "starts at the first position and moves the pointer until the end of the list, determining the lowest value." Textbooks often describe selection sort with two indices: one for the current boundary position and one for scanning the remainder of the list to find the minimum. The algorithm is simple and uses O(1) extra space, but it is inefficient for large lists because it performs O(n²) comparisons regardless of input order.
The other options are not standard algorithm names in typical computer science curricula. While many sorting algorithms exist (insertion sort, merge sort, quicksort, heap sort), "incremental," "progressive," and "pointer sort" are not canonical textbook algorithms in this context. Therefore, the correct answer is selection sort.
NEW QUESTION # 20
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