Technical Leadership and Architectural Influence Questions

Demonstrating leadership in technical decisions at the architecture or system level. Candidates should prepare concrete examples where they identified architectural problems, evaluated alternative solutions and trade offs, proposed a preferred design, gained buy in from engineers and stakeholders, and drove implementation. Discuss systems thinking and long term impact on team velocity, code quality, reliability, and product features. Include examples of championing new tools or frameworks, leading migrations or refactors, negotiating trade offs between time to market and technical debt, and occasions when you reversed a decision based on new data. Emphasize communication of complex technical ideas, consensus building with peers, and measurable outcomes.

HardTechnical

0 practiced

You propose migrating inter-service RPC from REST/JSON to gRPC/Protobuf to improve performance and type safety. Prepare a migration strategy that covers compatibility, incremental adoption, client SDK generation, developer onboarding, benchmarks to validate improvement, and rollback plan for partial adoption.

EasyTechnical

0 practiced

Explain API versioning strategies (URI versioning, header-based, content negotiation) and operational trade-offs for internal versus public APIs. Provide one example of a non-breaking change and one example of a breaking change and how you'd handle deprecation.

HardTechnical

0 practiced

Design a build-time and runtime API contract enforcement mechanism for multi-language microservices that prevents accidental breaking changes. Include CI contract tests (consumer-driven), schema validation, semantic versioning, publishing to a contract registry, and runtime guards to reject incompatible messages.

EasyTechnical

0 practiced

Compare eventual consistency and strong consistency in the context of APIs serving analytics and near-real-time dashboards. For analytics use cases, when would you choose eventual consistency? List compensating patterns you might implement (e.g., read-your-writes, causal reads, versioned snapshots).

MediumTechnical

0 practiced

How would you instrument tracing, metrics, and logs for a distributed enrichment service to enable efficient root-cause analysis of data-quality incidents? Prioritize what you would add first, describe sampling strategies for traces, and explain cost vs fidelity trade-offs.

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