Confluent·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Two coding problems in what felt like a single session at Confluent. One was a classic unix-y file utility, the other a grid traversal with a cost twist. Not the hardest interview I've done but the follow-ups on both kept things interesting.

Questions Asked (2)

Implement a tail(filePath, n) function that returns the last n lines of a file in their original order, handling edge cases like fewer than n lines, n=0, and trailing newlines. Also discuss how you'd handle very large files without reading everything into memory.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The basic version I got through fine, create a small test file, read lines, slice from the end, done.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then present a simple in-memory solution using a deque, followed by a scalable approach for large files using a circular buffer or reverse block reading. Emphasize trade-offs between simplicity and memory efficiency, and discuss handling of trailing newlines and n=0.

Pro tip: Mention that you'd use a deque with maxlen=n for the in-memory solution to automatically keep only the last n lines, and for large files, read the file backwards in blocks to avoid loading the entire file. This shows awareness of both Python's standard library and efficient I/O techniques.

1. Clarify requirements and edge cases

Ask about file size, encoding, and expected behavior for n=0, fewer than n lines, and trailing newlines. Confirm that lines should be returned in original order.

2. Design in-memory solution

Use a deque with maxlen=n to efficiently keep only the last n lines while reading the file line by line. This handles fewer than n lines and n=0 naturally.

3. Address large file handling

For very large files, propose reading the file backwards in blocks (e.g., using seek and read) to find the last n newlines without loading the entire file. Alternatively, use a circular buffer if reading forward.

4. Handle trailing newlines and edge cases

Decide whether to strip trailing newlines or preserve them. For n=0, return an empty list. Ensure that if the file has fewer than n lines, all lines are returned.

5. Discuss trade-offs and optimizations

Compare memory usage, time complexity, and code complexity of the in-memory vs. large-file approaches. Mention potential optimizations like using memory-mapped files or system calls like tail.

Key Points to Mention

Use of collections.deque with maxlen for O(n) memory and O(1) append/pop operations.
Reading file backwards in blocks to handle large files without loading everything into memory.
Edge cases: n=0 returns empty list; fewer than n lines returns all lines; trailing newlines should be handled consistently.
Time complexity: O(file size) for reading, but memory can be O(n) for in-memory or O(block size) for large files.
Trade-offs: simplicity vs. scalability; in-memory is simpler but not suitable for huge files.
Potential use of memory-mapped files or system utilities like tail for production code.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Given a 2D dungeon grid with a start cell, exit cell, walls, empty cells, and monster cells that cost 1 to enter, find the minimum total monster cost to travel from start to exit. Return -1 if unreachable. Follow-ups: return an actual path, and handle arbitrary non-negative costs per monster cell.

Algorithms & Data Structures

Author's notes

I went straight to BFS and the interviewer let me run with it for a bit before asking about the cost model.

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AI HintsAI Generated

Suggested Approach

Model the dungeon as a weighted graph where moving into a monster cell costs 1 and other moves cost 0, then run Dijkstra's algorithm (or 0-1 BFS for the binary cost case) from the start to the exit. For follow-ups, maintain parent pointers to reconstruct the path and generalize the cost function to handle arbitrary non-negative weights.

Pro tip: Mention that 0-1 BFS with a deque is optimal for the initial problem, but explicitly state that Dijkstra's algorithm is the general solution for arbitrary costs—this shows you understand the trade-offs and can adapt to follow-ups.

1. Clarify problem constraints and edge cases

Ask about grid dimensions, cost values, whether start/exit can be monsters, and if diagonal moves are allowed. Confirm that unreachable should return -1.

2. Model as a weighted graph

Treat each cell as a node and moves to adjacent cells as edges. Assign edge weight equal to the cost of entering the destination cell (0 for empty, 1 for monster, infinite for walls).

3. Choose the appropriate shortest-path algorithm

For binary costs (0 or 1), use 0-1 BFS with a deque for O(V+E) time. For arbitrary non-negative costs, use Dijkstra's algorithm with a priority queue.

4. Implement and track path if needed

Maintain a distance array and a parent pointer for each cell. After reaching the exit, backtrack from exit to start using parent pointers to reconstruct the path.

5. Analyze complexity and discuss optimizations

State time and space complexity: O(R*C) for 0-1 BFS, O(R*C log(R*C)) for Dijkstra. Mention early termination when exit is popped and potential use of A* if heuristic available.

Key Points to Mention

Weighted graph modeling: cost is incurred when entering a cell, not leaving.
0-1 BFS with deque for binary costs: push front for 0-cost edges, back for 1-cost edges.
Dijkstra's algorithm for arbitrary non-negative costs, using a priority queue.
Path reconstruction via parent pointers, handling the start cell correctly.
Time and space complexity analysis: O(R*C) vs O(R*C log(R*C)).
Edge cases: start equals exit, unreachable exit, walls blocking all paths.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.