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

0 practiced

Compare feature hashing and full-vocabulary encoding for extremely high-cardinality categorical features in production. Discuss trade-offs: memory footprint, collision risk and its effect on accuracy, interpretability, handling unseen categories, and how hashing interacts with drift detection and debugging.

HardTechnical

0 practiced

Debate centralized versus decentralized governance for feature and model versioning: a central metadata service that enforces versions and contracts versus delegating versioning to individual services. Discuss trade-offs in consistency, developer velocity, operational overhead, and approaches to enforce contracts and safe migrations at scale.

HardSystem Design

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Design monitoring and SLOs to detect fairness regressions and potential privacy leakage in a production ML model (CV or NLP). Specify metrics to compute, sampling strategies (how much and where to sample), alerting thresholds, remediation workflows, and the trade-offs between sensitivity (detecting true regressions) and alert noise.

EasyTechnical

0 practiced

Explain model quantization and how it affects inference latency, throughput, model size, and predictive accuracy. Give practical examples of when to use 8-bit quantization versus full-precision floats in production, and list common risks (e.g., accuracy drop on out-of-distribution inputs) and mitigations (calibration, mixed precision).

EasyTechnical

0 practiced

Explain canary deployment and describe how you would run a canary rollout for a new ML model version. Include which metrics to monitor (both system and model-quality), traffic ramp-up strategy, rollback criteria, and how to detect subtle correctness regressions during the canary.

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