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.

MediumTechnical

0 practiced

Propose a set of code-quality metrics for a data engineering repository (for example cyclomatic complexity, test coverage, lint warnings, PR review time). For each metric suggest realistic thresholds and describe the action to take when thresholds are not met. Explain why some metrics are more actionable than others.

MediumTechnical

0 practiced

How would you structure a complex analytical SQL query to maximize readability and maintainability? Describe use of CTEs, descriptive aliases, and modular SQL views. Provide a high-level structure for a query that computes daily active users and retention, and explain trade-offs between readability and raw performance.

HardTechnical

0 practiced

Design an async Python component that reads messages from a network socket, processes them, and publishes results to an external service with limited throughput. Explain how you would implement backpressure to avoid unbounded memory growth, which asyncio primitives you'd use (queues/tasks), and how to handle graceful shutdown and retryable vs non-retryable errors.

EasyTechnical

0 practiced

Implement a small, clean Python function `validate_record(record: dict) -> dict` that: checks required keys ['id','timestamp','value'], converts a datetime timestamp to an ISO8601 string when needed, ensures 'value' is a float (or raises ValueError), and returns a normalized dict. Keep the function focused, include a concise docstring, and avoid IO or side effects in the function body.

EasyTechnical

0 practiced

Explain what 'intent-revealing naming' means for a data engineering codebase. Provide three concrete examples: one for a Python function name, one for a SQL table name, and one for a dataset S3 path or Kafka topic. For each example show the bad/generic name and a better intent-revealing name and explain why the replacement is clearer for future readers and downstream teams.

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