# Drift

> Nothing changed. Everything moved.

Canonical URL: <https://datadriven.io/problems/drift>

Domain: AI Coding · Difficulty: hard · Seniority: L6

## Problem

Our RAG pipeline calls a hosted embedding endpoint. Last week the vendor silently upgraded the model under the same endpoint URL; cosine distances on a held-out probe set shifted by 18% mean, and product saw a precision drop on a fixed eval set. We have no alert for this. Run tests/test_drift_alarm.py to reproduce. Build a drift-detection check in monitoring/drift.py that runs daily and fires when the embedding distribution shifts beyond a defensible threshold. Write the threshold logic so an interviewer can defend the false-positive rate.

## Related

- [All practice problems](https://datadriven.io/problems)
- [Mock interview mode](https://datadriven.io/interview/drift)
- [Data Engineering Interview Prep Guide](https://datadriven.io/data-engineer-interview-prep)
- [Daily Challenge](https://datadriven.io/daily)

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