Program Level System Design Questions

Approaches system design from a program and delivery perspective. Candidates should explain how they clarify requirements and constraints up front, decompose complex systems into deliverable components and milestones, and plan schedules that account for technical complexity and dependencies. Describe how to involve and align engineering teams on architecture decisions, translate technical trade offs for stakeholders, identify and mitigate risks, set acceptance criteria, and plan for capacity, testing, deployment, and operational readiness. Include how program planning accounts for cross team coordination, technical debt, release coordination, and measurement of success.

MediumTechnical

0 practiced

Design a lightweight process and artifacts for cross-team data contracts and ownership for feature sharing in a medium-sized ML organization. Address schema versioning, backward-compatibility rules, contract validation automation, owners and SLAs for feature delivery, and escalation paths when contracts break.

HardSystem Design

0 practiced

Design a gated rollout plan for a high-risk ML model subject to fairness and regulatory constraints (for example: credit scoring or medical triage). Include offline fairness checks, pre-release independent audits, staged rollouts with subgroup performance monitoring, explicit approval gates, logging and evidence for auditors, and post-release remediation workflows.

MediumTechnical

0 practiced

An ML inference endpoint must support 10,000 QPS with a p95 latency under 50ms. Outline a capacity-planning approach: how you would benchmark, select instance sizes, set autoscaling rules, decide when batching is appropriate, use caching, and evaluate cost vs latency trade-offs. Also describe validation steps to prove the plan in staging.

EasyTechnical

0 practiced

Explain the difference between model-level acceptance criteria (for example: precision/recall thresholds, fairness checks, generalization tests) and program-level acceptance criteria (for example: deployment readiness, operational SLOs, business KPI lift). Provide examples of how a failing model-level test should or should not block a program-level gate.

EasyTechnical

0 practiced

Describe what 'program-level system design' means for machine learning initiatives. Explain how it differs from a single-model technical design and list the typical responsibilities, artifacts, and stakeholders you would expect a program lead to produce (for example: roadmap, milestones, acceptance gates, dependency matrix, runbooks, and stakeholder communication plan).

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