Decagon·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Decagon SWE interview focused on extending a rolling-window CSAT tracker with an update operation. Pretty design-heavy for a coding round, less about grinding algorithms and more about thinking through data structure tradeoffs.

Questions Asked (1)

You have a rolling-window CSAT tracker that supports record and get_average. Add an update method that modifies a conversation's score in place if it's still within the active window, and does nothing if the record has already been evicted. Walk through how you'd implement this efficiently.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The baseline tracker was already given so you're not building from scratch, which I appreciated.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data structures and constraints: the rolling window, how records are stored, and what 'active window' means. Then propose a design that supports O(1) update by maintaining a hash map from conversation ID to its position in the window (e.g., a deque or circular buffer), and handle eviction by removing from the map. Discuss trade-offs like memory overhead and concurrency, and walk through the update logic step-by-step.

Pro tip: Mention that you'd use a lazy deletion or timestamp check to avoid scanning the entire window on each update, and that you'd consider thread-safety if the tracker is accessed concurrently. This shows you think about real-world performance and reliability.

1. Clarify requirements and constraints

Ask about the window size, whether it's time-based or count-based, expected update frequency, and concurrency needs. Confirm that update should only affect records still in the window.

2. Choose data structures

Propose a combination: a deque (or circular buffer) to maintain order and evict old records, a hash map from conversation ID to the record's node/position for O(1) access, and a running sum for O(1) get_average.

3. Implement update logic

On update, look up the conversation ID in the hash map. If not found (evicted), do nothing. If found, adjust the running sum by the difference between new and old score, and update the record in place.

4. Handle eviction and window maintenance

When adding a new record, evict the oldest if the window is full, remove it from the hash map, and subtract its score from the running sum. Ensure update doesn't interfere with eviction.

5. Discuss trade-offs and edge cases

Cover memory overhead of the hash map, potential race conditions if concurrent, and how to handle duplicate updates or invalid scores. Mention alternatives like a balanced BST if order statistics are needed.

Key Points to Mention

Use a hash map for O(1) lookup of conversation records by ID.
Maintain a running sum to achieve O(1) get_average.
Eviction should remove the record from both the deque and the hash map.
Update should be O(1) by adjusting the running sum and modifying the record in place.
Consider thread-safety with locks or concurrent data structures if needed.
Discuss memory trade-offs: hash map adds overhead but enables fast updates.

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