Strategic Technical Decision Making Questions
Focuses on higher level, organization impacting technical decisions and direction setting. Candidates should discuss evaluating long term implications, aligning technology choices with company strategy, managing uncertainty in multi year decisions, balancing innovation with operational risk, and communicating strategic rationale to leadership and across teams. Examples should show decisions that affected architecture, platform direction, or major product technical choices.
HardTechnical
0 practiced
Compare the long-term implications of standardizing on a Python-first ML stack versus adopting compiled inference runtimes (C++/Rust) for production inference. Discuss developer productivity, runtime performance, interoperability, testing and maintenance burden, talent hiring, and potential ecosystem lock-in over a 5 to 10 year horizon.
EasyTechnical
0 practiced
Design a concise prediction API contract for an online scoring service. Specify required request fields, expected types, idempotency keys, correlation headers for tracing, response fields (including model version and confidence), schema versioning hints, and error semantics. Explain why each field is important for robust distributed operation.
MediumTechnical
0 practiced
How would you set SLOs and define error budgets for ML-powered product features such as recommendation click-through rate or prediction consistency? Describe a process to convert business KPIs into technical SLOs, how to allocate error budgets, and how to use them to balance innovation versus reliability.
MediumTechnical
0 practiced
Your team must decide whether to serve models on GPUs or CPUs. As a data scientist, propose a decision framework that accounts for throughput, latency percentiles, batchability, cost per inference, model size, and projected growth for the next 2-3 years. Include what benchmarks and data you would collect to support the decision.
MediumSystem Design
0 practiced
Design a robust API and model versioning strategy to support multiple active model versions simultaneously for A/B testing, blue-green, and gradual rollouts. Discuss contract evolution, automatic schema validation, compatibility testing, and how to coordinate client libraries, documentation, and backward-compatible changes.
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