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50 DSA Interview Questions with Answers Related to Time & Space Complexity Analysis
When it comes to preparing for DSA (Data Structures and Algorithms) interviews, understanding time and space complexity is crucial. It not only helps you evaluate the efficiency of your solution but also demonstrates your problem-solving skills to interviewers. Here’s a guide to 50 DSA interview questions focused on analyzing time and space complexity.
1. What is Time Complexity?
Answer: Time complexity refers to the amount of time an algorithm takes to run relative to the input size. It’s usually expressed in Big O notation (O(n), O(log n), O(n²), etc.) to describe the upper bound of execution time.
2. What is Space Complexity?
Answer: Space complexity refers to the amount of memory an algorithm uses relative to the input size. Like time complexity, it’s usually expressed in Big O notation.
3. What does Big O notation represent?
Answer: Big O notation represents the upper bound of an algorithm’s time or space complexity. It describes the worst-case scenario of the algorithm’s performance as the input grows.
