Programming Fundamentals and Code Quality Questions

Encompasses core programming skills, data structures, basic algorithms, language fundamentals, and code quality practices. Expect proficiency with arrays, strings, lists, hash maps or dictionaries, sets, common collection operations, basic sorting and searching algorithms, and tradeoffs between data structures. Understand control flow, functions and modular design, classes and object oriented programming concepts including encapsulation, inheritance, and polymorphism, exception handling, file input and output, and common language idioms for mainstream interview languages such as Python, Java, and C plus plus. Emphasizes writing clean, readable, maintainable code: meaningful naming, modular functions, small interfaces, handling edge cases and errors, logging and documentation, simple testing and debugging strategies, and awareness of time and space complexity for common operations. Candidates should be able to implement correct solutions, follow language specific idioms where appropriate, and demonstrate attention to code quality and readability.

EasyTechnical

0 practiced

Describe object oriented encapsulation and its benefits when organizing data preprocessing and feature engineering code in a data science project. Provide a short class-level design sketch for a 'FeatureTransformer' object that validates input, applies transformations, and tracks applied steps for reproducibility.

HardSystem Design

0 practiced

Design an efficient algorithm to compute the cosine similarity between a query sparse vector and a large collection of sparse vectors stored on disk. Discuss data representation on disk, indexing or pruning strategies to avoid scanning all vectors on every query, and space-time tradeoffs.

MediumTechnical

0 practiced

Explain the difference between shallow copy and deep copy in Python and give a practical data science example where shallow copying a dataset leads to an unintended side effect during in-place normalization of features.

MediumTechnical

0 practiced

Explain how the time and space complexity of common operations differ between arrays, linked lists, and balanced binary search trees. Provide concrete guidance for which data structure you would use in a feature store index that needs fast ordered range queries and frequent inserts.

EasyBehavioral

0 practiced

Describe the principles of clean code you apply when sharing Python scripts with non-technical stakeholders, focusing on naming, modularity, logging, and documentation. Provide one concrete example of a poor function name and a refactored version with improvements.

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