DSA Greedy Algorithms - Practice Questions 2026

About this course

Master Greedy Algorithms: Data Structures and Algorithms Practice ExamsWelcome to the most comprehensive practice resource designed to help you master Greedy Algorithms within the Data Structures and Algorithms (DSA) landscape. Whether you are preparing for high-stakes technical interviews at FAANG companies or aiming to solidify your competitive programming skills, these practice exams provide the rigorous environment you need to succeed.Why Serious Learners Choose These Practice ExamsIn the world of DSA, understanding the "Greedy Choice Property" and "Optimal Substructure" is one thing, but applying them under pressure is another. Serious learners choose this course because it moves beyond surface-level theory. We focus on the intuition behind local optimization and how it leads to global solutions. With a massive original question bank, mobile compatibility, and instructor support, this is the definitive toolkit for anyone committed to technical excellence.Course StructureThis course is meticulously organized into six distinct levels to ensure a smooth but challenging learning curve:Basics / Foundations: This section focuses on the fundamental logic of greedy approaches. You will encounter questions regarding sorting dependencies, basic exchange arguments, and identifying when a greedy strategy is applicable versus when it fails.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

Course objectives

• to master the application of greedy algorithms
• to practice with a variety of algorithmic problems in a rigorous environment
• to develop intuition for when greedy approaches succeed or fail