The structure they gave looked like [{"id":"1"}, {"deadline":2}, {"subtasks":["2","3"]}] and you had to figure out where 'consumed' fits and what it even means in context.
Start by clarifying the requirements and constraints of the 'consumed' field, such as whether it tracks per-subtask usage or aggregate consumption, and how it should be updated. Then propose a data model extension that balances simplicity, scalability, and consistency, considering the ML pipeline's needs for tracking task dependencies and resource usage.
Pro tip: Discuss how the 'consumed' field could be used for monitoring and debugging ML workflows, and mention the importance of idempotent updates to avoid double-counting in distributed systems.
Ask questions to understand what 'consumed' means: is it a boolean flag, a count, or a timestamp? Does it track each subtask individually or aggregate? Consider read/write patterns and consistency needs.
Suggest adding a 'consumed' field to the task object, possibly as a map from subtask id to consumption details (e.g., count, timestamp). Alternatively, consider a separate consumption log for auditability.
Explain how updates to 'consumed' will be handled: atomic operations, transactions, or eventual consistency. Discuss idempotency and conflict resolution in distributed settings.
Compare embedding vs. referencing, and discuss impact on query performance, storage, and complexity. Consider indexing strategies for efficient lookups.
Tie the design to ML workflows: tracking data usage, feature consumption, or resource allocation. Mention how this supports reproducibility and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.