Approach

To effectively answer the question "How would you write a function to merge two sorted arrays into one sorted array?", follow this structured framework:

1. Understand the Problem: Clarify the requirements and constraints.

2. Choose the Right Algorithm: Consider the efficiency of your approach.

3. Write the Code: Implement your solution with clear logic.

4. Test the Function: Validate your code with sample inputs.

Key Points

• Clarity: Make sure your explanation is straightforward and easy to follow.

• Logic: Show your reasoning and thought process behind the implementation.

• Efficiency: Discuss the time and space complexity of your approach.

• Edge Cases: Address potential edge cases that your function should handle.

Standard Response

Here’s a comprehensive answer demonstrating how to merge two sorted arrays into one sorted array:

def merge_sorted_arrays(arr1, arr2):
 # Create a new array to hold the merged result
 merged_array = []
 i, j = 0, 0

 # Loop until we reach the end of either array
 while i < len(arr1) and j < len(arr2):
 if arr1[i] < arr2[j]:
 merged_array.append(arr1[i])
 i += 1
 else:
 merged_array.append(arr2[j])
 j += 1

 # If there are remaining elements in arr1, add them
 while i < len(arr1):
 merged_array.append(arr1[i])
 i += 1

 # If there are remaining elements in arr2, add them
 while j < len(arr2):
 merged_array.append(arr2[j])
 j += 1

 return merged_array

# Example usage
array1 = [1, 3, 5]
array2 = [2, 4, 6]
result = merge_sorted_arrays(array1, array2)
print(result) # Output: [1, 2, 3, 4, 5, 6]

• Initialization: Two pointers, i and j, start at the beginning of arr1 and arr2, respectively.

• Comparison: Compare elements from both arrays and append the smaller one to merged_array.

• Remaining Elements: After the loop, append any remaining elements from either array.

• Return the Result: The function returns a single sorted array.

Explanation:

Tips & Variations

Common Mistakes to Avoid:

• Ignoring Edge Cases: Make sure to handle cases where one or both arrays are empty.

• Inefficient Approaches: Avoid nested loops, which can lead to higher time complexity (O(n^2)).

Alternative Ways to Answer:

• Using Built-in Functions: You could use Python's built-in sorted() function to combine and sort both arrays, but this would not be optimal in terms of efficiency.

Role-Specific Variations:

• Technical Roles: Focus on time complexity, memory usage, and edge cases.

• Creative Roles: Emphasize problem-solving and the creativity behind the approach.

• Managerial Roles: Discuss how this function could be useful in larger projects or systems.

Follow-Up Questions:

• How would you optimize this function for larger datasets?

• Can you explain the time and space complexity of your solution?

• How would you handle duplicate elements in the arrays?

Conclusion

By following this structured approach, you can effectively demonstrate your coding skills and problem-solving abilities during technical interviews. Remember to articulate your thought process clearly, handle edge cases, and discuss the efficiency of your solution to leave a positive impression on your interviewer. This comprehensive guide equips you with the tools needed to tackle similar coding questions in your job search and career growth