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

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You must choose an index for fast multi-attribute filtering over billions of rows (e.g., predicates on country, device_type, and date). Evaluate composite B-tree indexes, bitset indexes, and inverted indexes by attribute. Discuss query patterns that favor each and explain how bitmap indexes enable fast intersection operations.

MediumTechnical

0 practiced

Implement (in Python) an LRU cache class that supports get(key) and put(key, value) operations in O(1) time and a fixed capacity. Provide details about which underlying data structures you choose and why they achieve O(1) operations. You do not need to write full code but outline the methods and data structures precisely.

HardTechnical

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You must implement deduplication of very large user event IDs across time windows, but exact deduplication is too memory intensive. Sketch a hybrid approach combining a small in-memory Bloom filter and a disk-backed exact store to minimize false-positive drops while keeping memory bounded. Explain the trade-offs.

EasyTechnical

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Discuss how value vs reference types in different languages (e.g., Python objects vs. numpy scalars, Java primitive types) influence data structure selection for large-scale numeric arrays used in model training. Provide practical advice for a data scientist to avoid memory bloat and boxing/unboxing costs.

MediumTechnical

0 practiced

A model serving endpoint must look up user cohorts and quickly compute aggregated statistics. You can precompute and store cohort aggregates in a cache. Compare eviction policies: LRU, LFU, and TTL-based eviction. For each policy, explain the data structures used to implement them efficiently and trade-offs for accuracy and freshness.

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