SWE

Step 1: I recognized this as an interval overlap counting problem, which can be efficiently solved using the line sweep algorithm.

Step 2: I created two separate arrays — one for all start times and another for all end times — and sorted both in ascending order.

Step 3: I explained that we can merge these sorted arrays into a single event array, where each event has a time and a type (start = +1, end = -1), then sort the events by time. For events occurring at the same time, I placed start events before end events.

Step 4: I wrote code to iterate through these sorted events, maintaining a counter that increased by 1 for each start event and decreased by 1 for each end event. This counter represented the number of active stalls at any given time.

Step 5: During the sweep, I tracked the maximum value of this counter, which represented the maximum number of simultaneously operating stalls.

Step 6: When asked about the time complexity, I explained that it is O(N log N) due to the sorting step, followed by a linear scan through the events.

Step 1: I recognized this as a variation of the previous problem, requiring separate tracking for two different categories.

Step 2: I proposed separating the input data into two arrays — one for local vendors and one for guest vendors.

Step 3: For each category, I applied the same line sweep algorithm used in Problem 1: create event arrays with +1 for start events and -1 for end events, sort them, and perform the sweep.

Step 4: I maintained two separate counters — one for local vendors and one for guest vendors — and tracked the maximum value for each.

Step 5: I explained that this could also be solved with a single sweep by using a combined event array containing all events, while adding a type field to each event. This would allow us to increment the appropriate counter based on the vendor type.

Step 6: When asked about the time complexity, I explained that it remains O(N log N) due to the sorting step, even with the additional categorization.

Step 7: I also discussed how this approach could be generalized to handle any number of categories if needed.

Step 1: I recognized this as a dynamic programming problem, where we need to make optimal decisions at each point to maximize final energy.

Step 2: I defined a state DP[i][j] to represent the maximum energy Sinbad can have at point i on day j.

Step 3: For each day and each point, we have two choices: move forward (if enough energy) or stay at the current position.

Step 4: I formulated the recurrence relation:
If moving: DP[i+1][j+1] = max(DP[i+1][j+1], DP[i][j] - energy cost + energy[i+1])
If staying: DP[i][j+1] = max(DP[i][j+1], DP[i][j])

Step 5: I initialized DP = E (initial energy) and filled the DP table using bottom-up approach.

Step 6: I explained the time complexity as O(N²), where N is the number of points, and space complexity as O(N²).

Step 7: The interviewer asked me to optimize, and I suggested that we could reduce the state space by observing that Sinbad would never benefit from staying at a point for multiple consecutive days.

Tip 1: Use the STAR method (Situation, Task, Action, Result) to structure your response with a clear, concrete example.
Tip 2: Emphasize communication, empathy, and understanding the root cause before taking action.
Tip 3: Demonstrate both leadership skills and a willingness to collaborate in finding solutions.

Tip 1: Focus on your analytical approach to balancing different stakeholder needs while keeping the company's goals in mind.
Tip 2: Highlight your ability to gather diverse perspectives and incorporate feedback before making decisions.
Tip 3: Demonstrate your skills in communicating difficult decisions effectively and managing the aftermath.