Paper - II (vii) — Algorithms is a Programming & Data Structures (Senior CI) topic in the RAS/RPSC syllabus. This page gathers exam-style Paper - II (vii) — Algorithms multiple-choice questions with correct answers and explanations, so aspirants can test recall and revise frequently examined concepts.
Practice 44 Paper - II (vii) — Algorithms multiple-choice questions with detailed answers and explanations. Ideal for RAS/RPSC exam preparation.
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Q1. For sufficiently large n, which time complexity grows faster: O(n log n) or O(n^2)?
Explanation
As n becomes large, n squared grows faster than n log n because n/log n tends to increase without bound. This is why an O(n log n) sorting algorithm is usually preferred over an O(n^2) one for large inputs.
Q2. For a binary tree, which traversal visits the left subtree first, then the root node, and then the right subtree?
Explanation
Inorder traversal of a binary tree is defined by the sequence left subtree, root, and right subtree. This makes it especially important for binary search trees, where it lists keys in sorted order.
Q3. What does Big-O notation primarily express in algorithm analysis?
Explanation
Big-O notation is used to describe an upper bound on the growth of an algorithm resource such as time or memory as input size increases. It suppresses constant factors and lower-order terms for large-input comparison.
Q4. Which problem is a standard example where a greedy strategy gives an optimal solution when edges are chosen by increasing weight with cycle checks?
Explanation
Kruskal's algorithm is a classic greedy algorithm for the minimum spanning tree problem. It repeatedly selects the smallest edge that does not create a cycle, and this safe-choice rule leads to an optimal spanning tree.
Q5. If an algorithm has running time O(n), what does this notation primarily express?
Explanation
O(n) states that beyond some input size, the running time is bounded above by a constant multiple of n. It is about growth rate, not an exact step count.
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