Technical Vision and Strategy Questions
Covers long term technical direction, architecture choices, infrastructure and platform strategy, and how technical roadmaps align with business goals. Interviewers will probe your perspective on where technology is heading, major architectural trade offs, cloud and modernization approaches, and how you would shape the organization or team to meet future needs. At senior levels this includes strategic thinking beyond immediate problems, influencing cross team technical initiatives, prioritization of long term investments, and communicating a coherent technical roadmap.
MediumSystem Design
0 practiced
Draft a clear API contract for a prediction microservice consumed by multiple teams. Include: endpoint paths, input schema (types and validation rules), output schema including confidence/metadata, batch inference options, error codes and semantics, rate limits, and authentication expectations.
HardTechnical
0 practiced
Architect how to expose feature-store semantics to online microservices to ensure reproducible predictions: include feature versioning, schema contracts, default handling if features are missing, and guarantees for online/offline parity without redesigning core data-pipeline systems.
HardTechnical
0 practiced
Your production model's performance degrades compared to training because preprocessing in microservices is non-deterministic (e.g., random seeds, different tokenization settings, floating-point differences). Propose architectural and process changes to ensure deterministic inputs, schema contracts, CI tests, and a test harness to catch regressions before deployment.
EasyTechnical
0 practiced
As a Data Scientist, explain the differences between online (real-time) inference and batch (offline) inference. Discuss latency, throughput, freshness, consistency, typical system architectures, cost implications, and concrete business use-cases where each approach is preferred (e.g., fraud detection vs nightly scoring).
MediumTechnical
0 practiced
Describe a robust model versioning and rollout process for production. Cover artifact packaging, metadata (training data, code, hyperparameters), backward compatibility guidance, shadow testing, canary promotion rules, automated promotion criteria, and rollback mechanics that minimize client disruption.
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