Architecture and Technical Trade Offs Questions

Centers on system and solution design decisions and the trade offs inherent in architecture choices. Candidates should be able to identify alternatives, clarify constraints such as scale cost and team capability, and articulate trade offs like consistency versus availability, latency versus throughput, simplicity versus extensibility, monolith versus microservices, synchronous versus asynchronous patterns, database selection, caching strategies, and operational complexity. This topic covers methods for quantifying or qualitatively evaluating impacts, prototyping and measuring performance, planning incremental migrations, documenting decisions, and proposing mitigation and monitoring plans to manage risk and maintainability.

HardTechnical

26 practiced

Design an architecture for Retrieval-Augmented Generation (RAG) with long-context streaming responses. Address where to place retrieval caches, how to manage retrieval latency vs model token budget, and strategies for partial response generation while waiting for slow retrievals.

HardSystem Design

37 practiced

Design a disaster recovery plan for AI workloads across regions. Cover: model artifact storage (checkpoints), feature data, in-flight requests, DNS/routing failover, testing of DR drills, and RTO/RPO targets. Explain trade-offs between hot, warm, and cold standby strategies.

MediumTechnical

34 practiced

Database/metadata choice: For storing model metadata, experiment results, and deployment events (high write rate from CI and read-heavy queries for monitoring), describe the factors that govern the choice between a relational DB, document store, and time-series DB. Focus on consistency, query patterns, and operational complexity.

HardTechnical

35 practiced

Plan an incremental migration of a monolithic recommender to an event-driven microservice architecture with minimal user-impact. Provide a migration timeline, validation gates for each stage, how to keep model outputs consistent across versions, and rollback plans if new components fail.

HardSystem Design

33 practiced

Design monitoring, alerting and automated mitigation for model performance regressions and data drift. Include what signals trigger automatic rollback, how to separate signal noise from true degradation, and a playbook for human-in-the-loop investigation.

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