Code Quality & Technical Communication Questions

Best practices and principles for writing clean, maintainable code and communicating technical decisions clearly. Topics include code quality metrics, code reviews, refactoring, static analysis, testing strategies related to maintainability, documentation standards, API/documentation practices, and effective communication of design and architecture decisions.

EasyBehavioral

56 practiced

Tell me about a time when you improved code quality on a data science project. Describe the Situation, the Task you owned, the Actions you took (specific practices, tools, or policies), and the measurable Results. If you haven't had this experience, describe a hypothetical but realistic plan you would execute.

EasyTechnical

62 practiced

You're onboarding a new engineer to reproduce results from your data science repo. Outline a README template that covers environment setup (conda/pip), data access steps (how to get datasets), commands to run preprocessing, training, evaluation, and how to validate outputs. Include how to document expected results and common troubleshooting steps.

MediumTechnical

44 practiced

Explain the trade-offs between unit tests, integration tests, and end-to-end tests for ML systems in terms of speed, flakiness, maintenance cost, and bug-detection capabilities. Given limited testing budget, where would you invest test coverage for the highest ROI in a model pipeline?

EasyTechnical

62 practiced

Write a Python function normalize_columns(df: pd.DataFrame) that normalizes DataFrame column names to snake_case, strips leading/trailing whitespace, replaces non-alphanumeric characters with underscores, coerces non-string column names into stable names (e.g., 'col_123'), and ensures uniqueness (append suffixes for collisions). Provide a brief example: input columns ['Customer ID', 'Total$Amount', 123] -> expected ['customer_id', 'total_amount', 'col_123']. The function must be idempotent (calling it twice yields the same result) and handle pandas Index types.

HardTechnical

53 practiced

You are tasked with creating a standardized code-review checklist that works across data science and ML engineering teams that have slightly different priorities (research vs production reliability). Draft the checklist items that must be common and explain how you would reconcile differing expectations and enforce the checklist pragmatically.

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