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.
MediumTechnical
0 practiced
Discuss the practical trade-offs of using an eventually consistent feature store (e.g., stream-updated store with lag) versus a strongly consistent store for online features. How would each choice affect model correctness, freshness, latency, and serving-layer complexity? Suggest patterns to mitigate eventual-consistency pitfalls.
EasyTechnical
0 practiced
You must choose a model serving framework (e.g., TensorFlow Serving, TorchServe, custom Flask/gRPC) for a medium-scale image classification API. List the evaluation criteria you would use (latency, throughput, ease of deployment, observability, GPU support), describe how to run a quick proof-of-concept benchmark, and explain how team expertise affects the final choice.
HardTechnical
0 practiced
A fraud detection product uses an ensemble of three models to boost accuracy but latency SLOs are tight. Discuss ensemble versus single-model trade-offs (accuracy, maintainability, resource cost). Propose an efficient serving architecture for ensembles (parallel evaluation, early-exit cascades, prioritized evaluation) and how to evaluate whether the ensemble benefit justifies added complexity.
HardTechnical
0 practiced
As a Staff Machine Learning Engineer, you're asked to deliver a low-latency, globally distributed personalization system in 3 months with minimal budget and a small team. How would you push back constructively, propose pragmatic alternatives, prioritize scope, and document trade-offs to stakeholders? Provide a concrete step-by-step approach including minimal viable architecture, staging plan, and communication strategy.
MediumSystem Design
0 practiced
Describe differences and trade-offs between A/B testing, shadowing (traffic mirroring), canary/blue-green, and feature-flag-based model rollouts. For a high-risk fraud-detection model, recommend a rollout strategy, monitoring metrics, and specific rollback/governance criteria.
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