Strategic Technical Decision Making Questions
Focuses on higher level, organization impacting technical decisions and direction setting. Candidates should discuss evaluating long term implications, aligning technology choices with company strategy, managing uncertainty in multi year decisions, balancing innovation with operational risk, and communicating strategic rationale to leadership and across teams. Examples should show decisions that affected architecture, platform direction, or major product technical choices.
HardSystem Design
0 practiced
Design a global disaster recovery and failover plan for stateful model services that rely on large embedding indices (>100GB). Discuss RTO/RPO targets, replication topology (active-active vs active-passive), cold vs warm standby strategies, embedding index warming, and how to keep user experience acceptable during failover.
EasyTechnical
0 practiced
You're designing a new ML-backed feature. Should you decompose into separate microservices for feature extraction, model inference, and post-processing, or keep it as a single monolithic service? Consider deployment independence, team ownership, scaling characteristics, latency budgets, operational overhead, testing complexity, and failure isolation. Provide decision criteria and a recommended approach for a 5-person ML team supporting three product teams.
HardTechnical
0 practiced
Design an incident-response and postmortem process tailored to AI systems that can fail silently (e.g., model drift causing subtle business metric degradation). Include detection signals, on-call playbooks, automated rollback triggers, postmortem data collection (model inputs/outputs, model version), and mechanisms to prevent recurrence and share learnings across teams.
HardSystem Design
0 practiced
Design an edge deployment strategy for inferencing on tens of millions of devices: decide build vs buy for runtime, OTA updates, rollback mechanisms, signing and encryption of model artifacts, resource-constrained model optimization (quantization, pruning), and monitoring/reporting strategies that respect device bandwidth and user privacy.
EasyTechnical
0 practiced
Explain eventual consistency and describe how it affects design choices for model metadata stores, feature caches, and personalized inference caches. Give concrete examples of cases where eventual consistency is acceptable (e.g., personalization recommendations) and where strong consistency is required (e.g., billing, permission checks). Discuss mitigation patterns when eventual consistency causes user-visible issues.
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