Start by clarifying requirements and scale, then design a high-level architecture that covers data ingestion, model serving, decisioning, action orchestration, and audit storage. Emphasize reliability, security, and failure handling, and discuss trade-offs between latency, accuracy, and cost.
Pro tip: Focus on the end-to-end flow and failure modes rather than deep ML details; Google values system thinking and operational excellence. Propose a phased rollout with human-in-the-loop for high-risk actions to build trust and safety.
Ask about the number of machines, snapshot frequency, data size, latency requirements, and action criticality. Clarify what 'abnormal' means and the cost of false positives/negatives.
Outline how OS snapshots are collected (agent-based), transported (message queue), and processed (feature extraction, normalization). Consider batch vs. streaming and storage for raw and processed data.
Describe how models are trained, deployed, and served (e.g., TensorFlow Serving, Vertex AI). Explain how predictions are turned into decisions using thresholds, rules, or ensembles, and how to handle model updates.
Design a workflow engine to trigger actions (shutdown, quarantine, email) with idempotency, retries, and rollback. Store all decisions and actions in an audit log for querying and compliance.
Implement authentication/authorization (e.g., OAuth, IAM), encryption, and audit trails. Discuss failure scenarios (model errors, action failures) and mitigation (circuit breakers, dead-letter queues, manual approval).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.