Technical Innovation and Modernization Questions

Covers leading and executing technical change that raises the engineering bar while preserving operational stability. Topics include identifying and prioritizing innovation opportunities, sponsoring research and experimentation, running proofs of concept and pilots, and introducing new tools or frameworks. Also includes strategies for modernizing legacy systems and architecture with minimal business disruption, managing technical debt, migration planning, rollback and cutover approaches, and maintaining reliability and continuity. Evaluated skills include optimizing performance and cost at scale, establishing engineering standards and best practices, governance and risk management, stakeholder alignment and communication, measuring impact and return on investment, and balancing long term innovation with short term pragmatism.

EasyTechnical

0 practiced

Explain the differences between blue-green deployment and canary deployment when updating ML model services in production. Provide concrete scenarios for when you would prefer each approach, explain how they impact rollback procedures, traffic split mechanics, monitoring needs, and how they integrate with feature flags and A/B testing frameworks.

EasyTechnical

0 practiced

Implement a simple Python FastAPI endpoint that receives JSON features, validates the input schema using Pydantic, calls a placeholder 'predict(features: dict) -> dict' function, measures inference latency, and returns a JSON response containing the prediction, model_version header, and latency_ms. Focus on request handling, validation, and latency measurement (you may mock the model).

MediumTechnical

0 practiced

Serverless model serving reduces ops overhead but sometimes has unacceptable cold-start latency. Propose architectural and operational strategies to reduce cold-starts for serverless ML inference (e.g., provisioned concurrency, lightweight models, warmers), and discuss trade-offs in cost and complexity.

HardTechnical

0 practiced

You're leading a cross-team decision whether to adopt a new ML framework that promises faster iteration. Propose a decision framework that balances experimentation speed vs operational stability, including pilot criteria, risk mitigation (e.g., canaries, backward compatibility layers), training for engineers, and rollback triggers.

HardTechnical

0 practiced

Under strict regulatory constraints, design a reproducible model deployment pipeline that provides tamper-evident audit logs, stores explainability artifacts, guarantees artifact immutability, and supports quick rollback to a prior audited release. Include how you'd store artifacts, what metadata to record, and how to surface audit trails to regulators.

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