Technical Debt Management and Refactoring Questions

Covers the full lifecycle of identifying, classifying, measuring, prioritizing, communicating, and remediating technical debt while balancing ongoing feature delivery. Topics include how technical debt accumulates and its impacts on product velocity, quality, operational risk, customer experience, and team morale. Includes practical frameworks for categorizing debt by severity and type, methods to quantify impact using metrics such as developer velocity, bug rates, test coverage, code complexity, build and deploy times, and incident frequency, and techniques for tracking code and architecture health over time. Describes prioritization approaches and trade off analysis for when to accept debt versus pay it down, how to estimate effort and risk for refactors or rewrites, and how to schedule capacity through budgeting sprint capacity, dedicated refactor cycles, or mixing debt work with feature work. Covers tactical practices such as incremental refactors, targeted rewrites, automated tests, dependency updates, infrastructure remediation, platform consolidation, and continuous integration and deployment practices that prevent new debt. Explains how to build a business case and measure return on investment for infrastructure and quality work, obtain stakeholder buy in from product and leadership, and communicate technical health and trade offs clearly. Also addresses processes and tooling for tracking debt, code quality standards, code review practices, and post remediation measurement to demonstrate outcomes.

MediumSystem Design

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Design a CI pipeline for an ML project that stores datasets in object storage, trains models on GPU instances, and serves models in Kubernetes. The pipeline must provide fast developer feedback, prevent bad models from reaching production, and minimize cost. Detail stages, tools, gating rules, where to run heavy tests (nightly vs PR), and policies for dependency/driver version checks.

MediumTechnical

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Propose a set of quantitative metrics and a measurement approach to quantify the impact of technical debt on training cost and retrain frequency. Include formulas or example calculations for: cost-per-retrain, wasted GPU hours due to failed runs, and an index that correlates debt signals (test flakiness, build time) with retrain delays.

EasyTechnical

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Define "technical debt" specifically for AI/ML systems. Describe at least five distinct types (for example: code debt, data debt, model debt, pipeline/infrastructure debt, and configuration/ops debt). For each type give a concrete AI-specific example (e.g., ad-hoc data-cleaning scripts, undocumented feature engineering, untested model conversion), and explain why each type tends to accumulate faster in AI projects compared with traditional software projects.

EasyTechnical

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List and explain at least five metrics to track code and architecture health over time in AI systems (for example: cyclomatic complexity, test coverage, mean build time, training-time per epoch, incident frequency). For each metric, recommend monitoring frequency and an alerting policy or threshold example.

EasyTechnical

0 practiced

Explain the difference between an incremental refactor and a full rewrite in the context of ML systems. Provide three ML-specific scenarios where an incremental refactor is preferable and three scenarios where a rewrite is justified. For each scenario list key risks and how you would mitigate them.

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