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| Section | Weight | Objectives |
|---|---|---|
| Discrete Mathematics & Logic | 25% | - Set theory, relations, functions - Propositional and predicate logic - Boolean algebra and digital logic - Proof techniques and mathematical induction |
| Computer Architecture & Organization | 15% | - Von Neumann architecture - Memory hierarchy and performance - Instruction sets and execution cycles - CPU, memory, I/O systems |
| Data Structures | 20% | - Primitive and composite data types - Data storage and retrieval principles - Arrays, linked lists, stacks, queues - Trees, graphs, hash tables |
| Software Engineering & Programming Basics | 15% | - Programming paradigms - Testing and debugging fundamentals - Basic syntax and control structures - Software development lifecycle |
| Algorithms & Complexity | 25% | - Sorting and searching algorithms - Recursion and iterative structures - Algorithm design and analysis - Big O notation, time and space complexity |
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NEW QUESTION # 62
What is the time complexity of a quicksort algorithm?
Answer: D
Explanation:
Quicksort is a divide-and-conquer sorting algorithm. It works by selecting a pivot element, partitioning the array into two subarrays (elements less than the pivot and elements greater than the pivot), and then recursively sorting those subarrays. In the average case, the partition step splits the array into roughly equal halves, so the recurrence is commonly written as (T(n) = T(n/2) + T(n/2) + O(n)), where (O(n)) is the cost of partitioning. This solves to (O(n \log n)), which is why quicksort is widely taught as an efficient general- purpose sorting method.
However, textbooks also emphasize that quicksort has a worst-case time complexity of (O(n
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