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.

MediumSystem Design

0 practiced

Design a release plan for deploying a new ML model to production where product, infra, and data teams are stakeholders. The plan should cover pre-release checkpoints, canary or blue/green rollout strategy, monitoring signals to watch, rollback triggers and procedures, and stakeholder communication cadence. Include who signs off at each stage.

MediumTechnical

0 practiced

Outline how to design, execute, and measure hundreds of A/B experiments across multiple teams without contamination. Discuss experiment registry, consistent randomization, sample-size planning, overlap handling, multiple-testing correction, and governance to ensure experiments remain independent and interpretable.

MediumTechnical

0 practiced

Your manager asks you to reduce time-to-market by 30% for an ML feature. Propose concrete program and process changes (for example: minimum viable scope, parallel workstreams, pre-approved infra, faster experiment cycles, and incremental deployment) including resourcing and risks, and indicate how you would measure the impact of each change.

EasyTechnical

0 practiced

Describe how you would choose and align success metrics for an ML program tasked with improving user engagement while also meeting latency and cost constraints. Provide at least three business-level KPIs and three model/infra metrics, and explain how you would trade off improvements in one area against regressions in another.

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.

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