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

0 practiced

You're presented with a Spark codebase where business logic lives in notebooks and many Python UDFs are applied row-by-row causing performance problems. Describe a refactor plan to: (a) move logic into a modular codebase, (b) prefer DataFrame/native APIs over UDFs, (c) write small pure functions for unit testing, and (d) maintain readability and backward compatibility during rollout. Include testing and deployment considerations and trade-offs.

EasyTechnical

0 practiced

As a new data engineer on a team, which style guides and automated tools would you recommend adopting to maintain consistent Python code (for example PEP8, black, mypy)? Describe how you would integrate these tools into developer workflows (local tooling, pre-commit hooks, CI) and what minimal set of rules you would enforce first to get quick wins.

MediumTechnical

0 practiced

Implement a Python decorator `retry_on_exception(max_retries: int = 3, backoff_secs: float = 0.5, exceptions: tuple = (Exception,), is_retryable: Optional[Callable[[Exception], bool]] = None)` that retries the wrapped function on specified exceptions using exponential backoff up to max_retries. Ensure the decorator preserves function metadata and is testable. Show the implementation and a short explanation of choices.

MediumTechnical

0 practiced

Describe clean-code practices for logging inside data processing functions. Cover log levels (debug/info/warn/error), structured logging fields to include (job_id, run_id, dataset), avoiding sensitive data, and where to use logs vs metrics. Give short examples of what to log at different levels.

MediumSystem Design

0 practiced

Design a clear Python package layout for a production batch ETL pipeline that ingests raw data, transforms it, and writes to a warehouse. Include module names for configuration, IO, transforms, schemas/types, CLI entrypoint, and tests. Explain why you placed responsibilities where you did and how this structure enforces separation of concerns and small focused functions.

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