Software Developer

Step 1: First, I considered checking all possible subarrays using two loops and calculating their sums to see if any equaled K. This approach works but has a time complexity of O(n²), which is not efficient for large inputs.

Step 2: Then, I recalled that this can be optimized using prefix sums and a hash map. I calculated cumulative sums while traversing the array and stored the first occurrence of each prefix sum.

Step 3: At each index, I checked whether (currentSum − K) already existed in the map. If it did, it meant that the subarray between those two indices had a sum of K, and I updated the maximum length accordingly.

Step 4: This reduced the time complexity to O(n) with extra space for the hash map, which the interviewer accepted as the optimal solution.

Step 1: First, I understood that this problem is related to the sliding window technique since we are dealing with subarrays and a condition on distinct elements.

Step 2: I started implementing a sliding window using two pointers and a frequency map to track how many times each element appears in the current window.

Step 3: While expanding the right pointer, I updated the sum and the frequency map. When the number of distinct elements exceeded K, I attempted to shrink the window from the left.

Step 4: I was able to maintain the window and the distinct count, but I faced difficulty in correctly updating the maximum sum in some edge cases, especially when duplicate elements were involved and when shrinking the window multiple times was required.

Step 5: Due to time constraints, I could not fully debug and finalize the optimal solution. However, the interviewer discussed the correct approach and explained how careful window adjustments and sum updates would solve the issue efficiently.