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 must deduplicate user events arriving from multiple ingestion pipelines where event IDs can be out-of-order and you cannot afford to store all seen IDs in memory. Explain how a Bloom filter can help, including false-positive implications for deduplication in analytics versus billing systems. Discuss size vs. false-positive trade-offs.

MediumTechnical

0 practiced

Design a data layout (row-oriented vs columnar) for storing a large analytics table used in training models and performing ad-hoc queries. Discuss read/write patterns, compression, CPU cache effects, and which format (e.g., Parquet, CSV, HDF5) you'd prefer for offline model training versus interactive exploration.

MediumTechnical

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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.

EasyTechnical

0 practiced

Explain when a kd-tree is appropriate for nearest neighbor search and when it fails (curse of dimensionality). Provide practical thresholds or diagnostics a data scientist can use to decide between kd-tree and approximate methods for embedding vectors of varying dimension (e.g., 2D, 32D, 256D).

MediumTechnical

0 practiced

A service must provide per-user sampling of high-cardinality events for analytics with bounded memory. Compare options: maintaining reservoir samples per user, using a global sample with per-user post-filtering, or probabilistic sampling keyed by hashed user ID. Explain biases, memory cost, and downstream analysis implications.

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