Design a distributed machine learning system for processing large-scale graph data.

Instruction: Explain how you would architect the system to efficiently process and analyze graph data at scale, including data storage, model training, and inference. Fictional brief: daily fraud-risk ranking on a 100-million-node graph with one billion edges; features change hourly, the scoring deadline is 06:00, and four workers each have 64 GB RAM. Only edges with event_time before the scoring cutoff are eligible. Provide a baseline, partition/sampling plan, versioned artifact and missed-deadline fallback.

Context: This question assesses the candidate's understanding of the challenges and solutions for working with graph data at scale, including distributed computing and specialized algorithms.

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I would first clarify what the graph workload is: node classification, link prediction, recommendations, fraud, or some other task. Graph systems can become overengineered quickly if you do not tie the architecture to the actual objective and scale profile.

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