Confluent·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Confluent SWE interview with a pretty involved coding problem around IoT sensor health monitoring. The core challenge was designing a class that could efficiently answer repeated queries about sensor stability across a fixed time window.

Questions Asked (1)

Design a SensorHealth class that takes a log of IoT sensor pings (each with a sensor ID and timestamp) and answers queries about whether a given sensor is STABLE or UNSTABLE at a given time T. The window starting at T is divided into 5 consecutive 60-second slots. A slot is inactive if no pings fall in it for that sensor, and the sensor is UNSTABLE if there are 3 or more consecutive inactive slots. Optimize for repeated queries.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

My first instinct was to just scan all pings on every query and that obviously wasn't the point.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem: define the 5-slot window, what constitutes an inactive slot, and the threshold for instability. Then propose a solution that preprocesses the log to enable fast queries, such as sorting pings per sensor and using binary search to check slot activity, or precomputing a timeline of active slots. Discuss trade-offs between preprocessing time and query time, and consider edge cases like multiple pings in a slot and queries outside the log range.

Pro tip: Mention that you would preprocess the log by grouping pings by sensor and sorting timestamps, then for each query, use binary search to determine active slots in O(log n) per slot, or precompute a bitmask of active slots per sensor for O(1) queries. This shows you optimize for repeated queries.

1. Clarify requirements and constraints

Ask about the expected number of sensors, pings, and queries, and whether the log is static or streaming. Confirm the definition of a slot (60 seconds starting at T) and that a ping at exactly T+60k falls into slot k.

2. Design data structures for efficient queries

Group pings by sensor ID and sort timestamps. For each sensor, store a sorted list of ping times. Optionally, precompute a boolean array or bitmask indicating which 60-second slots (relative to a global epoch) have at least one ping.

3. Implement query logic

For a query (sensor, T), check each of the 5 slots: slot i covers [T+60i, T+60(i+1)). Use binary search on the sensor's ping times to see if any ping falls in that interval. Count consecutive inactive slots and return UNSTABLE if count >= 3, else STABLE.

4. Optimize for repeated queries

If queries are frequent, precompute for each sensor a list of active slot indices (e.g., slot index = floor(timestamp/60)). Then for a query, determine the 5 slot indices and check membership in a hash set or use a bitset. This reduces query time to O(5) = O(1).

5. Analyze trade-offs and edge cases

Discuss time/space trade-offs: preprocessing O(N log N) to sort, O(N) space for slot sets. Handle edge cases: no pings for sensor, pings exactly on slot boundaries, queries before first ping or after last ping, and multiple pings in same slot.

Key Points to Mention

Time complexity: preprocessing O(N log N) for sorting, query O(log N) with binary search or O(1) with precomputed slot sets.
Space complexity: O(N) to store pings and O(N) for precomputed slot sets.
Use of binary search or hash sets for fast slot activity checks.
Handling of slot boundaries: a ping at time t belongs to slot floor((t - T)/60) if we consider the window starting at T.
Edge cases: empty log, sensor with no pings, queries outside the range of logged pings.
Trade-off between preprocessing time and query time, and suitability for streaming vs static logs.

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