Clean Code and Best Practices Questions

Covers the principles and hands on practices that produce readable, maintainable, and reliable code. Core elements include intent revealing and consistent naming, small focused functions and classes that follow single responsibility, avoiding duplication through refactoring and appropriate abstractions, clear structure and separation of concerns, following language specific idioms and style guides, consistent formatting, concise comments that explain nonobvious intent, defensive programming and robust error handling, edge case handling and input validation, use of linters and static analysis, incremental refactoring techniques, and pragmatic trade offs between ideal design and delivery constraints. Interviewers will also probe involvement in code reviews, version control hygiene, code metrics, and how candidates advocate for and teach coding standards to peers.

HardTechnical

81 practiced

You need to port a performance-critical Python loop to Rust/Cython to meet latency requirements for a streaming inference pipeline. Outline a pragmatic plan that preserves clean code, tests, and deployment ease: identify the hot-path, define a clear interface, write thorough tests in Python, implement native extension, and integrate into CI. Discuss fallback strategies if native extension fails in an environment.

HardTechnical

122 practiced

Design a system to detect and recommend refactors in a data science repo using static analysis and historical change data. Describe inputs (AST, git history, test failures), rules/ML features, scoring, and how recommendations are surfaced to engineers (PR comments, dashboards). Consider false positives and scalability.

MediumTechnical

77 practiced

Write a Python function validate_json_schema(records: List[dict], schema: dict) -> dict that checks each record against a provided simple schema (field name, required bool, expected type) and returns a summary: counts of valid, invalid per-field errors, and a small sample of invalid records. Focus on clear code and helpful error messages.

MediumTechnical

130 practiced

Pandas memory use can explode on large datasets. Explain three best-practice strategies to reduce memory usage when loading large CSVs with pandas and provide short example code for downcasting numerics and converting suitable columns to categorical.

MediumTechnical

78 practiced

You must enforce consistent error handling across a team's codebase. Propose a small hierarchy of custom exceptions for data-processing pipelines (examples: DataValidationError, ExternalServiceError, RetryableError), how they'd be used, and how to document and enforce their usage via code review and linters.

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