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

You find a slow loop that computes pairwise distances between two arrays using Python for-loops. Rewrite the computation using numpy vectorized operations and explain the readability vs speed trade-offs and when you would accept the loop for clarity.

HardTechnical

0 practiced

You need to make a long-running model training job resumable and robust to failures. Describe a checkpointing strategy for state (model weights, optimizer state, data iterator position), atomic writes for checkpoints, and how to resume deterministically. Discuss trade-offs around checkpoint frequency and storage costs.

HardTechnical

0 practiced

Floating point and BLAS non-determinism causes subtle reproducibility differences between CPU and GPU runs. As a principal data scientist, explain how you would document, test, and mitigate platform-dependent differences so that engineers and stakeholders understand acceptable tolerances and when discrepancies require investigation.

HardTechnical

0 practiced

Floating-point differences and non-determinism can break reproducible pipelines. Explain strategies to achieve reproducible results across platforms: seed-setting, deterministic algorithms, controlled BLAS settings, and tolerance-based tests. Provide examples of tests that assert reproducibility within acceptable tolerances.

EasyTechnical

0 practiced

Describe good version-control hygiene for data science projects. Cover: commit granularity and messages, branch naming, handling large data or model artifacts, and ways to prevent secrets from being committed. Include recommended git workflows for small teams (2-10 engineers).

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