AI and Machine Learning Background Questions
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.
EasyTechnical
63 practiced
Define a feature store and describe why Site Reliability Engineers should be involved in operating it. Cover availability SLAs, online/offline parity, low-latency access, consistency guarantees, and monitoring/alerting considerations.
HardTechnical
122 practiced
For a multi-model inference platform, compare operational trade-offs between batching requests to improve GPU throughput versus serving single-request low-latency. Discuss scheduling, queueing latency, priority dispatch, tail latency impacts, and cost modeling.
MediumTechnical
66 practiced
Write a SQL query that joins a predictions table to a labels table to compute daily rolling AUC per model_version over the last 7 days. Provide the expected schema for both tables and discuss performance considerations for large datasets.
MediumSystem Design
78 practiced
Design an end-to-end CI/CD pipeline for ML models that runs data validation, automated training, evaluation against holdout sets, fairness checks, and deployment to a staging canary. Describe release gates, artifacts to store (model binary, manifest, metrics), and rollback mechanisms.
MediumTechnical
70 practiced
You observe an increase in tail latency (p99) for model inferences correlated with a spike in feature-store read latency. As SRE, design an investigation plan, immediate mitigations, and a permanent fix path that addresses both infra and software changes.
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