SDE - Intern

Step 1: First, I considered the brute-force method, where we check every possible subarray and calculate its sum. This works but takes O(N²) time.

Step 2: Since the problem was on a coding platform with large constraints, the brute-force approach was not efficient.

Step 3: I optimized it by using the prefix sum + hashmap technique. While iterating through the array, I maintained the cumulative sum and checked if (currentSum - K) existed in the hashmap. If yes, it meant a subarray with sum K was found, and I updated the maximum length.

Step 4: This gave me an efficient O(N) time and O(N) space solution, which passed all test cases on the platform.

Polymorphism specifically refers to the ability of a method or object to take multiple forms, enabling dynamic method binding or adopting multiple behaviors with a unified interface.

For a balanced BST like AVL or Red-Black Tree, the height is O(log n), so search times also follow O(log n) in the worst case.

Concurrency control ensures that transactions execute in a serializable manner, avoiding issues like lost updates or dirty reads, thus preventing interference.

Step 1: My first thought was to find the minimum by traversing the stack each time getMin() is called, but that would take O(N) time.

Step 2: I then thought of keeping an extra variable to track the minimum, but the problem arose when the minimum element was popped.

Step 3: To optimize, I maintained an auxiliary stack alongside the main stack. For every push, I stored the new minimum in the auxiliary stack. For every pop, I removed the top from both stacks. This way, getMin() always returns the top of the auxiliary stack in O(1) time.

Step 4: Implemented this logic, tested with sample inputs, and it worked as expected.

Step 1: First, I created a base class (e.g., Shape) with common attributes, including an area() function.

Step 2: Then, I implemented inheritance by creating derived classes such as Circle and Rectangle that overrode the area() method, demonstrating polymorphism.

Step 3: I applied encapsulation by keeping class data members private and accessing them through getters and setters.

Step 4: Finally, I created objects of these derived classes and demonstrated runtime polymorphism using a base class pointer.

Step 1: Initially, I considered using nested loops to check every pair of elements, but that would take O(N²) time.

Step 2: Then, I optimized the approach by using a hash set or unordered_map to keep track of visited elements. If an element appeared again, I marked it as a duplicate. This reduced the complexity to O(N) with extra space.

Step 3: For a further optimized solution (if asked), I considered marking elements in the array itself (by negating the value at the corresponding index), which can find duplicates in O(N) time and O(1) space.

Tip 1: Mention how AI is already shaping everyday life (healthcare, automation, recommendations, virtual assistants).
Tip 2: Highlight both opportunities (efficiency, better decision-making, new jobs) and challenges (job displacement, ethics, data privacy).
Tip 3: Conclude with a balanced view: AI will act as a support system for humans, and adapting to it will be the key to success.

Tip 1: I explained that such issues often arise due to dependency or environment mismatches between systems.
Tip 2: I suggested using Docker to containerize the application so it runs the same way on any machine, regardless of OS or environment.
Tip 3: I also mentioned documenting dependencies (via requirements.txt or package.json) as a backup but emphasized Docker as the most reliable solution for portability.

Tip 1: Keep the answer professional and aligned with the company’s growth (avoid mentioning frequent job switching).
Tip 2: Show eagerness to learn and grow, both technically and in terms of responsibilities.
Tip 3: Emphasize that you see yourself contributing to impactful projects, possibly mentoring juniors, and being an integral part of the organization.