Role Team and Infrastructure Questions Questions
Guides asking targeted questions about the specific role, team responsibilities, and the technical or operational infrastructure that supports the role. Topics include typical responsibilities, on call rotations or support models, current infrastructure challenges, tech stack or tooling, success metrics for the role, collaboration with adjacent teams, opportunities for growth, and infrastructure priorities. This helps candidates demonstrate role understanding and probe for operational and strategic expectations.
HardSystem Design
0 practiced
Define SLIs and SLOs appropriate for a production ML system that includes both model scoring and underlying serving infrastructure. Explain how you would set error budgets, formulate alerting thresholds, and integrate SLO-driven incident response and postmortems with SRE processes.
EasyBehavioral
0 practiced
Describe ways you have helped upskill teammates on ML infra topics (workshops, documentation, office hours, code reviews). Provide measurable impact if possible, such as reduction in onboarding time, fewer incidents, or faster release cycles.
MediumTechnical
0 practiced
Your organization is deciding whether to centralize an ML platform team or let individual product teams manage their own models. What are the pros and cons of each approach, what decision criteria would you recommend, and how would you design a hybrid model if needed?
MediumSystem Design
0 practiced
How would you plan capacity and autoscaling for a model serving cluster that must maintain P95 latency under 100ms while supporting traffic spikes up to 10x baseline? Discuss load testing, autoscaling policies, warm pools, caching strategies, and deployment practices to meet latency SLOs.
MediumSystem Design
0 practiced
Design an ML CI/CD pipeline that supports training, validation, model artifact storage, reproducibility, tests, and automated deployment. Describe stages (data validation, unit tests, integration tests, model evaluation), tooling you would pick (examples: Airflow, Argo, GitHub Actions, MLflow), and how you would support reproducible retraining and safe rollbacks.
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