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In my technical interview, I was asked an interesting problem based on queues and operation constraints.
The interviewer gave multiple queues and mentioned that the pop() operation is very expensive (like a remote network call), while isEmpty() is allowed. We were not allowed to directly access the size of the queue, and it was okay to destroy the queues during processing.
The task was to find the length of the shortest queue.
Initially, I thought of traversing each queue completely and counting elements, but the interviewer pushed me to think about optimizing the number of pop() operations. Since pop() was costly, a brute-force approach was not ideal.
I then discussed an optimized approach where we process all queues in parallel and keep track of how many pops are performed level by level. As soon as any queue becomes empty, we can stop and return the count at that point, since that would correspond to the shortest queue. This avoids unnecessary pops on longer queues.
The interviewer seemed satisfied with the approach, especially the focus on minimizing expensive operations and early stopping.
This question tested my ability to:
Overall, it was a great learning experience on writing efficient and practical algorithms.
In my 2nd technical interview, I was given a graph-based problem involving cities and travel times.
The interviewer described a map where cities are connected via roads, each having a known travel time. I was starting from a source city (City A) and was given a list of favorite cities. The task was to determine which of these favorite cities could be reached in the minimum time.
At first glance, it looked like a typical graph traversal problem, but the interviewer emphasized efficiency and correctness in handling weighted paths.
I quickly identified that this problem can be modeled as a weighted graph, where:
Cities are nodes Roads are edges with weights (travel time)
I proposed using Dijkstra’s Algorithm to compute the shortest distance from the source city to all other cities. Once the shortest paths were computed, I iterated through the list of favorite cities and selected the one with the minimum distance.
We also discussed edge cases like:
Unreachable cities Multiple cities having the same minimum time Graph representation (adjacency list vs matrix)
The interviewer further asked about time complexity and optimization, where I explained the use of a min-heap (priority queue) to achieve (O((V + E)\log V)).
This problem tested my understanding of:
Graph modeling and shortest path algorithms Choosing optimal algorithms based on constraints Handling real-world scenarios like maps and travel
###Follow-up Question
The interviewer then extended the problem:
Now assume you must pass through an intermediate city (City B). Find the fastest reachable favorite city under this constraint.
To solve this, I modified the approach:
Run Dijkstra from source city A → compute distances to all nodes Run Dijkstra from city B → compute distances from B to all nodes For each favorite city F, compute: distance(A → B) + distance(B → F) Choose the favorite city with the minimum total distance
This ensures that the path always goes through city B while still being optimal.
Overall, it was a classic but insightful application of Dijkstra’s algorithm in a practical scenario.