Evaluate the candidates ability to solve complex multi layered technical and design problems by making reasonable assumptions, articulating trade offs, and handling edge cases. Candidates should show how to decompose problems that span networking caching persistence and performance optimization, select architectures and algorithms with explicit trade off analysis such as speed versus simplicity and functionality versus performance, and consider failure modes including network failures device limitations and concurrent access patterns. Strong responses include clear assumption statements, alternative approaches, complexity and cost considerations, testing and validation strategies, and plans to monitor and mitigate operational risks.
EasyTechnical
0 practiced
You're responsible for instrumenting latency SLOs for a model inference endpoint that must meet p95 < 200ms and p99 < 500ms. Describe the metrics, tags, trace points, collection points (client vs server), dashboards, alerting policies, and investigation runbook you would create to monitor and diagnose latency regressions.
MediumSystem Design
0 practiced
Given a microservices architecture where a scoring service calls a feature retrieval service and a model inference service, design API contracts and idempotency semantics to allow safe retries and prevent duplicate side effects. Specify request/response shape, idempotency keys, retry idempotency guarantees, and error handling patterns.
HardTechnical
0 practiced
Design a reproducibility system for model experiments across compute clusters. Define required metadata (code hashes, container images, hyperparameters, random seeds, data versions), lineage tracking for datasets and models, how to store artifacts and commands to reproduce runs, and approaches to enforce deterministic runs across environments.
HardTechnical
0 practiced
Design a system to detect training-serving skew where production feature distributions diverge from training data, causing model degradation. Describe data collection, statistical tests and thresholds, alerting rules, labelling and retraining pipelines, and automated vs manual remediation strategies.
HardTechnical
0 practiced
Design an A/B testing and experimentation system for a generative AI feature (assistant responses) across millions of users. Requirements: preserve statistical validity, minimize exposure to harmful outputs, collect labeled and implicit feedback, support staged rollouts, and enable rollback on regressions. Describe assignment strategy, metrics, logging, privacy considerations, and analysis pipeline.
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