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.
HardTechnical
0 practiced
Design a layered defense strategy to detect and mitigate adversarial attacks on an image-classification inference service. Cover model-level defenses (adversarial training, robust architectures), input sanitization, ensembles, anomaly monitoring, runtime detection, and operational mitigations. Discuss trade-offs between safety, latency, and false positives.
MediumTechnical
0 practiced
Design a logging and observability plan for an AI inference pipeline to enable diagnosis of model regressions and performance incidents. Specify what metrics, logs, traces, sampled payloads to collect, retention policy, sampling strategy, privacy/anonymization considerations, dashboards, and alerting thresholds you would implement.
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.
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