SDE - 1

nput: nums = [2, 7, 11, 15], target = 9
Output: [0, 1]
Explanation: nums[0] + nums[1] = 2 + 7 = 9

Input: nums = [-2,1,-3,4,-1,2,1,-5,4]
Output: 6
Explanation: The subarray [4, -1, 2, 1] has the largest sum = 6.
Step 1: I first thought of a brute-force solution by checking all possible subarrays using two nested loops, which would take O(n²) time.

Step 2: I realized this approach would be inefficient for large inputs and needed optimization.

Step 3: Then I recalled Kadane’s Algorithm, where we use Dynamic Programming to maintain the maximum sum ending at each index.

Step 4: I maintained two variables: currentSum and maxSum. For each element, I decided whether to start a new subarray or extend the existing one.

Step 5: This reduced the time complexity to O(n) and the solution was accepted.

Tip 1: Read Operating System concepts thoroughly from standard books like Galvin (Operating System Concepts) and revise definitions regularly.

Tip 2: Focus on understanding core concepts like synchronization, deadlock, paging, and memory management instead of rote learning.

Tip 3: Practice explaining concepts with real-world examples (e.g., semaphore using producer-consumer problem, virtual memory using RAM vs disk analogy).

Step 1: I started by clarifying the requirements, both functional and non-functional, such as scalability, availability, and low latency.

Step 2: I identified core components, including the API layer, database, and encoding strategy.

Step 3: I proposed using a key-value database to store the mapping between short URLs and long URLs.

Step 4: I discussed how to generate unique short keys using Base62 encoding.

Step 5: The interviewer asked scalability-related follow-ups, such as handling millions of requests and database bottlenecks, which helped me understand areas where I needed deeper preparation.