SDE - 1

Step 1: Understand the Problem 
The task is to find two indices in an array such that their corresponding values sum up to a given target. Each input is guaranteed to have exactly one solution, and you cannot use the same element twice.

Step 2: Initial Approach (Brute Force)
Initially, a straightforward approach would be to use a nested loop to check every possible pair of indices in the array. For each pair, check if their values add up to the target.
- Time Complexity:** O(n²), where n is the number of elements in the array.

Step 3: Optimize with Hash Map 
To improve efficiency, use a hash map (or dictionary) to keep track of the indices of the elements you have seen so far. As you iterate through the array:
- For each element, calculate the complement (i.e., `target - current_element`).
- Check if this complement exists in the hash map. If it does, you’ve found the solution.
- If not, store the current element's index in the hash map and continue.

Step 4: Implementation

Step 5: Test the Solution
Verify the solution with different test cases to ensure it correctly handles various scenarios:
- Arrays with positive numbers
- Arrays with negative numbers
- Arrays where the solution involves indices in different positions

Step 6: Review and Optimize
Ensure that the solution is both correct and efficient. The optimized approach using the hash map has a time complexity of O(n) and space complexity of O(n), making it suitable for large arrays.

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This stepwise approach demonstrates the problem-solving process, starting with a basic solution and then optimizing it for better performance.

Step 1: Understand the Problem

The problem involves finding the number of distinct islands in a grid where '1' represents land and '0' represents water.
An island is defined as a group of connected '1's, connected horizontally, vertically, or diagonally.
Step 2: Initial Approach

I started by thinking about traversing the grid and identifying groups of connected '1's.
A common approach for such problems is to use Depth-First Search (DFS) or Breadth-First Search (BFS) to explore and mark connected components.
Step 3: Implement DFS to Count Islands

I implemented a DFS approach to explore each cell of the grid.
For each unvisited '1', I initiate a DFS to mark the entire connected component (island) as visited.
I used an auxiliary boolean matrix to keep track of visited cells.
Step 4: Code Implementation

I defined a numIslands function to iterate over each cell of the grid.
If a cell contains '1' and has not been visited, I initiated a DFS from that cell and incremented the island count.
The DFS function (search) explores all 8 possible directions (horizontal, vertical, and diagonal) to find connected land cells.
Step 5: Handling Boundaries and Connections

In the search function, I checked if the current cell is out of bounds or already visited or water ('0'). If so, I returned without further processing.
If the cell is valid, I marked it as visited and recursively explored its neighbours in all 8 directions.
Step 6: Optimizations and Final Review

The DFS approach efficiently finds all connected land cells and counts islands with time complexity O(n * m), where n and m are the grid dimensions.
I reviewed the code to ensure it handled edge cases like grids with no land cells or grids with only one cell.

Be Authentic and Reflective:

Tip: Answer questions honestly and reflect on your true motivations and experiences. Authenticity helps build a genuine connection with the interviewer and demonstrates self-awareness.
Approach: Share personal stories and experiences that illustrate your values and career aspirations. Authentic responses are more memorable and relatable.

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Prepare Clear and Concise Responses:

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