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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.

EasyTechnical
88 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.
EasyTechnical
73 practiced
You're given a monolithic Python function that: (a) reads raw CSV from S3, (b) parses rows into typed records, (c) applies business filtering and enrichment, and (d) writes the result to Parquet. Describe a refactor into smaller functions or modules that follow the single responsibility principle. Provide high-level function signatures (no full implementation required) and explain how this layout improves testability and maintainability.
HardTechnical
75 practiced
Design and sketch a thread-safe bounded in-memory buffer for a high-throughput multi-producer, multi-consumer ingestion component. Requirements: bounded capacity, non-blocking 'offer' that drops or rejects when full according to policy, atomic counters for dropped and processed counts, and clear primitives to inspect buffer state. Provide pseudocode or Python and explain the concurrency primitives used and how you'd test for race conditions.
HardTechnical
70 practiced
In a distributed streaming system with at-least-once message delivery, multiple retries can create duplicate downstream writes. Design a robust, scalable approach to deduplicated writes to a downstream datastore (file store or DB) that accounts for idempotent keys, transaction support, late arrivals, and eventual compaction. Describe trade-offs and failure modes.
MediumTechnical
76 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.

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