Data Structure Selection and Trade Offs Questions

Skill in selecting appropriate data structures and algorithmic approaches for practical problems and performance constraints. Candidates should demonstrate how to choose between arrays lists maps sets trees heaps and specialized structures based on access patterns memory and CPU requirements and concurrency considerations. Coverage includes case based selection for domain specific systems such as games inventory or spatial indexing where structures like quadtrees or spatial hashing are appropriate, and language specific considerations such as value versus reference types or object pooling. Emphasis is on explaining rationale trade offs and expected performance implications in concrete scenarios.

MediumTechnical

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You need to support fast windowed aggregations on a high-rate time-series sensor stream (e.g., compute rolling sums and counts for many sensors). Compare using a ring buffer plus incremental aggregation, vs append-only logs with segment trees, vs approximate sketches. Discuss memory bounds, update time, and how you would support queries for arbitrary ranges and evictions.

EasyTechnical

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Compare sparse matrix formats (CSR, CSC, COO) versus dense ndarrays for ML workloads. For each format, describe memory layout, efficient operations (mat-vec, mat-mat), typical use-cases (e.g., sparse features, graph adjacency), and how conversion costs and GPU support affect your decision for training and inference.

HardTechnical

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Design a concurrent lock-free queue suitable for ML job scheduling in C++ where producers and consumers run on many threads and low-latency scheduling is required. Discuss algorithms (Michael-Scott queue), memory reclamation approaches, ABA problem, and how to test correctness under stress.

MediumTechnical

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Implement reservoir sampling (select k samples uniformly at random from a stream of unknown length) in Python. Provide code for single-pass sampling, explain correctness/probability guarantees, and discuss how to scale this to distributed streams where each worker maintains a reservoir.

MediumTechnical

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Compare row-oriented (row-major) vs columnar (column-major like Parquet/Arrow) storage for feature stores and offline training pipelines. Discuss IO patterns, compression benefits, vectorized processing, and how each affects training pipelines that read many features for many examples vs a few features for many examples.

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