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.

HardTechnical

0 practiced

Implement (high-level pseudocode) a function in Python that maintains a fixed-size reservoir sample per user to sample up to k events uniformly from an unbounded stream per user. Describe the data structure and its memory bounds and how you would support millions of users in a distributed environment.

EasyTechnical

0 practiced

A dataset contains many duplicate categorical values and you need to store membership and perform fast union/intersection operations across user segments. Would you use a set, list, or bitmap-like structure? Explain trade-offs in memory, speed, and vectorized operations for large-scale analytics (millions of users).

HardTechnical

0 practiced

You're building a geospatial join between events and regions (polygons). Choose appropriate spatial index data structures on both polygons and point events (R-tree, quadtree, spatial hash) and explain the trade-offs in indexing complexity, update cost for dynamic polygons, and query performance for point-in-polygon checks at scale.

MediumTechnical

0 practiced

You see model training slowing due to data-loading bottlenecks: the pipeline reads many small feature files per example. Propose data structure and storage layout changes (sharding, record batches, columnar bundles) to improve throughput and explain the trade-offs in flexibility vs. read efficiency.

MediumTechnical

0 practiced

Imagine you must store large sparse feature vectors for use in batch training and frequent similarity searches. Explain the trade-offs between CSR/CSC storage and a key→(index,value) inverted list per feature. Include costs for converting between formats and common ML ops like matrix-vector multiply.

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