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 the difference between shallow copy and deep copy in languages such as Python and Java. Provide concrete examples of when a shallow copy can lead to bugs in a data pipeline that mutates nested structures, and explain how to perform deep copies and their costs. Mention alternatives to deep copying at scale.
MediumTechnical
0 practiced
Implement in C++ a function lower_bound_index(const vector<int>& a, int target) that returns the first index i such that a[i] >= target or -1 if none. The implementation should be O(log n) and handle duplicates correctly. Provide tests including empty arrays and all-elements-less-than-target cases.
MediumTechnical
0 practiced
You have a 200GB newline-delimited JSON file containing user records with fields user_id and updated_at. Design an implementation to deduplicate the file so only the most recent record per user_id remains, given 4GB RAM. Outline algorithm, disk usage, how to split work, and trade-offs between external sort and hash-based partitioning.
HardTechnical
0 practiced
As a senior data engineer propose a coding standards and code review process tailored for a data engineering team that balances speed and quality. Include rules for naming, error handling, logging, required tests, PR size limits, CI checks (linters, unit tests), and onboarding steps. Provide measurable policies and how to enforce them practically.
EasyTechnical
0 practiced
Implement a Python function is_balanced(s: str) -> bool that checks whether parentheses are balanced for the characters '()[]{}'. This will be used for lightweight validation in a parser used inside an ingestion pipeline. Handle empty strings and unexpected characters and provide runtime complexity.
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