Design anomaly detection and response platform
GoogleSoftware Engineer · Technical Screen
Last updated
This question evaluates a candidate's competency in designing end-to-end ML-driven systems that handle large-scale, low-latency OS snapshot ingestion, binary anomaly detection, decisioning and action orchestration with multi-tenant security, auditability, and failure rollback.
A cloud service ingests operating system snapshots and runs an AI-based anomaly detector that outputs Normal or Abnormal. For Abnormal, the system can shut down or quarantine the machine and send warning emails; clients can query the history of warnings. Design the detection service: data collection, model serving, decisioning, action orchestration, audit and history storage, access controls, and failure rollback. Discuss latency, throughput, false positives, and safeguards for automated actions.
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