Production Readiness and Professional Standards Questions

Addresses the engineering expectations and practices that make software safe and reliable in production and reflect professional craftsmanship. Topics include writing production suitable code with robust error handling and graceful degradation, attention to performance and resource usage, secure and defensive coding practices, observability and logging strategies, release and rollback procedures, designing modular and testable components, selecting appropriate design patterns, ensuring maintainability and ease of review, deployment safety and automation, and mentoring others by modeling professional standards. At senior levels this also includes advocating for long term quality, reviewing designs, and establishing practices for low risk change in production.

EasyTechnical

37 practiced

You deployed a binary fraud model and need observability. Explain the differences between logs, metrics, and traces for diagnosing model issues in production. Give two concrete examples of what you'd collect for each signal and explain what kinds of questions each signal answers (for example, 'why did latency spike?' or 'did feature distributions change?').

EasyTechnical

34 practiced

Explain what a data contract between producer and consumer services is and why it's important for ML production readiness. Provide a simple example schema for a user_profile feature (fields, types, required), describe validation checks you would run, and outline how you would version and evolve the contract with minimal disruption.

EasyTechnical

33 practiced

List and explain production-relevant quality metrics for ML systems beyond raw accuracy. Include latency and throughput, calibration, false positive/negative costs, stability or volatility over time, feature-level data-quality metrics, and explain why each matters for reliability and business impact.

EasyTechnical

39 practiced

Explain the canary deployment pattern for ML models: how it works, why it's used, which metrics you should monitor during a canary, how to choose canary traffic fraction and duration, and how canary differs from blue-green and shadow deployments. Use a model serving example and include simple promotion/rollback criteria.

MediumSystem Design

42 practiced

Design a monitoring dashboard for a binary classification fraud model that handles 10k requests per minute and 500k predictions per day. Specify online metrics (prediction distribution, p95 latency, error rate), data metrics (feature drift scores, missing value rates), alert thresholds, sample queries or PromQL-like expressions for each metric, and an alerting policy describing severity and on-call steps.

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