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.

EasyTechnical

0 practiced

Explain the 'strangler pattern' for incremental refactors and give a concrete ML example where you would apply it (e.g., migrating a monolithic training job to a modular orchestration pipeline). Detail the integration strategy, data migration approach, testing stages, and rollback plan.

EasyTechnical

0 practiced

List and explain the most common root causes of technical debt in AI projects. Cover process, tooling, architecture, data-practices, and people-related causes. For each cause, give a concrete AI-specific example and one practical mitigation the team can apply immediately.

EasyTechnical

0 practiced

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.

MediumTechnical

0 practiced

How would you measure and improve test coverage specifically for ML-related code, including data transformation code, feature engineering logic, model evaluation wrappers, and serving code? Provide concrete steps to raise coverage, how to prioritize which areas to test first, and how to measure diminishing returns.

HardTechnical

0 practiced

Discuss advanced strategies to reduce inference latency and memory footprint for transformer-based models (e.g., quantization, pruning, knowledge distillation, architecture modification, batching and caching, ONNX/TensorRT optimizations). For each method explain trade-offs on model quality, test coverage you would add, and deployment considerations.

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