DSA Greedy Algorithms - Practice Questions 2026

Core Concepts: Here, we dive into classic problems. Expect detailed scenarios involving Activity Selection, Fractional Knapsack, and Egyptian Fractions. This level ensures you understand the standard greedy templates used in software engineering.

Intermediate Concepts: At this stage, we introduce graph-based greedy algorithms. You will be tested on your knowledge of Minimum Spanning Trees (Prim’s and Kruskal’s) and Single-Source Shortest Paths (Dijkstra’s), focusing on edge cases and complexity analysis.

Advanced Concepts: This level pushes your boundaries with complex scheduling problems, Huffman Coding, and Matroid Theory. These questions are designed to mimic the difficulty of "Hard" rated problems on popular coding platforms.

Real-world Scenarios: Greedy algorithms are not just for whiteboard interviews. This section covers practical applications such as data compression, networking routing protocols, and resource allocation in operating systems.

Mixed Revision / Final Test: The ultimate challenge. This full-length practice exam mixes all previous topics in a timed environment to simulate a real interview or exam setting, testing your ability to switch contexts rapidly.

OPTION 2: Because items can be broken into smaller units, allowing the knapsack to be filled exactly to its capacity with the most "dense" value.

OPTION 3: Because it explores all possible combinations of items like Dynamic Programming.

OPTION 4: Because the problem lacks the Optimal Substructure property.

OPTION 5: Because greedy strategies always work for 0/1 Knapsack problems as well.

OPTION 3: Greedy algorithms specifically do not explore all combinations; that is the hallmark of Brute Force or Dynamic Programming.

OPTION 4: This is incorrect; the problem does possess Optimal Substructure, which is why Greedy/DP can be considered.

OPTION 5: Greedy does not work for 0/1 Knapsack because you cannot take fractions, and a local optimal choice may prevent the global optimum.

OPTION 2: The node with the median frequency is placed at the root.

OPTION 3: The two nodes with the lowest frequencies are merged into a single parent node.

OPTION 4: Nodes are inserted into a Binary Search Tree alphabetically.

OPTION 5: The tree is balanced using AVL rotations after every insertion.

OPTION 2: The root is not determined by the median; it is the final node created after all merges.

OPTION 4: Huffman trees are based on frequency, not alphabetical order.

OPTION 5: Huffman trees are not necessarily balanced; they are structured based on weights (frequencies).

OPTION 2: Pick the edge with the maximum weight to ensure the graph remains connected.

OPTION 3: Select the shortest edge that connects a vertex in the growing MST to a vertex outside the MST.

OPTION 4: Randomly select an edge and check if it forms a cycle.

OPTION 5: Remove the heaviest edge from a cycle until no cycles remain.

OPTION 2: Picking the maximum weight would result in a Maximum Spanning Tree, not a Minimum one.

OPTION 4: Random selection does not guarantee a minimum weight solution.

OPTION 5: This is a "Reverse-Delete" approach, which is different from the incremental greedy approach of Prim's.

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