Architecture and Technical Trade Offs Questions

Centers on system and solution design decisions and the trade offs inherent in architecture choices. Candidates should be able to identify alternatives, clarify constraints such as scale cost and team capability, and articulate trade offs like consistency versus availability, latency versus throughput, simplicity versus extensibility, monolith versus microservices, synchronous versus asynchronous patterns, database selection, caching strategies, and operational complexity. This topic covers methods for quantifying or qualitatively evaluating impacts, prototyping and measuring performance, planning incremental migrations, documenting decisions, and proposing mitigation and monitoring plans to manage risk and maintainability.

MediumTechnical

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Database/metadata choice: For storing model metadata, experiment results, and deployment events (high write rate from CI and read-heavy queries for monitoring), describe the factors that govern the choice between a relational DB, document store, and time-series DB. Focus on consistency, query patterns, and operational complexity.

HardSystem Design

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Design monitoring, alerting and automated mitigation for model performance regressions and data drift. Include what signals trigger automatic rollback, how to separate signal noise from true degradation, and a playbook for human-in-the-loop investigation.

MediumSystem Design

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Design a CI/CD pipeline for ML models: include unit tests, dataset validation, model training triggers, model evaluation/gating, model registry promotion, and automated canary deploys. What guardrails prevent unsafe or low-quality models from reaching production?

HardTechnical

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Leadership scenario: You're the AI technical lead and two competing proposals are on the table: (A) aggressive architectural refactor to support a new real-time feature with high risk and long lead time, (B) incremental improvements that deliver partial value quickly. Describe how you'd assess technical and business trade-offs, make a recommendation, and communicate the decision to engineering, product, and exec stakeholders.

EasyTechnical

0 practiced

List the top 8 metrics you would instrument for a deployed ML model's serving endpoint (inference) and explain why each matters for identifying performance regressions or correctness issues. Include at least one metric related to model quality drift and one operational metric.

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