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.

HardSystem Design

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Design a compressed, persistent snapshot format for large model checkpoints that allows efficient random-access reads of individual layer tensors without decompressing the entire file. Specify data structures for index tables, chunking strategy, deduplication method, and how to balance chunk size to optimize IO throughput and seek overhead.

EasyTechnical

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Explain how a bitset (bitarray) can be used to represent presence/absence features efficiently in memory and how rank/select operations on compressed bitsets (Roaring, WAH) help accelerate queries. Provide examples of use in indexing and filtering for large-scale ML pipelines and the trade-offs compared to storing explicit lists of IDs.

MediumTechnical

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You need to sample items from a large dataset with weights that change over time (e.g., importance sampling). Compare using a binary indexed tree (Fenwick), segment tree (sum-tree), and reservoir sampling variants to support weighted sampling with updates. Discuss update and sample complexity and memory overhead for millions of items.

HardTechnical

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For extremely long-context transformers (e.g., up to 1M tokens), propose memory-efficient representations for attention masks and sparse attention patterns. Discuss compressed bitset representations, run-length encoding, block-sparse masks, and GPU constraints such as bank conflicts and shared memory limits. Estimate memory usage and algorithmic complexity of computing attention for your chosen approach.

EasyTechnical

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During preprocessing you must detect whether tokens in a streaming text corpus are duplicates within the current job. Explain the trade-offs between using an in-memory set, a sorted structure, and a probabilistic data structure (Bloom filter). Discuss memory usage, false-positive/false-negative characteristics, and implications for downstream model quality and retraining.

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