Duplicate curb-sensor events could inflate occupancy counts if the streaming job treats every delivery as unique. What concern does that raise?

Duplicate curb-sensor events that could inflate occupancy counts raise processing-semantics concerns: at-least-once delivery can create duplicates that double-count without idempotent dedupe or exactly-once handling. Streaming jobs must plan for replay and retry. Saying HDFS block size must equal sensor voltage is a nonsensical pairing of unrelated concepts. Claiming Kafka topics cannot exist in any city network is false. Asserting Spark drivers refuse all numeric counters by design is incorrect. Exam caveat: “exactly-once” is end-to-end—source, compute, and sink must cooperate; claiming the engine logo alone guarantees correct occupancy is risky. Operational check: replay a curb-sensor segment with intentional duplicates, confirm raw counts inflate, then enable dedupe keys or transactional sinks and re-verify occupancy metrics match the physical ground truth sample. In production terms for at-least-once duplicates and idempotent sinks, assign an owning team, wire a dashboard that shows the pattern is healthy under peak civic load, and require a staging drill that rehearses failure and recovery before resident impact. Revisit the choice when traffic shape, retention, or integrity needs change, and keep a named rollback owner on the change record. Re-test both the happy path and the failure path for at-least-once duplicates and idempotent sinks after material dependency upgrades so regressions are caught before the next public release.