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

86 practiced

Design a system for approximate counting and frequency estimation for high-rate event streams. Compare Count-Min Sketch, Count Sketch, HyperLogLog, and exact hashmaps. Discuss error bounds, memory per key, mergeability across shards, and how to tune parameters to target specific error rates and tail guarantees.

EasyTechnical

122 practiced

Explain how a Bloom filter works and give an ML-specific use case (e.g., preventing duplicate negative samples or avoiding repeated expensive feature lookups). Describe false positive vs false negative behavior, how to choose the bit array size and number of hash functions, and how to merge Bloom filters in distributed systems.

MediumTechnical

70 practiced

Describe the engineering trade-offs between storing dense model weights as float32, float16, bfloat16, and 8-bit quantized integers. Discuss memory, compute support on hardware (GPUs, TPUs), numerical stability during training and inference, and when mixed precision is acceptable.

HardTechnical

58 practiced

Design a lock-free concurrent hashmap appropriate for a parameter server where many threads read and some threads write. Discuss algorithms (e.g., split-ordered lists, hopscotch hashing, optimistic concurrency), memory reclamation (hazard pointers, epoch GC), and trade-offs versus sharded locked maps.

EasyTechnical

79 practiced

Compare arrays (contiguous memory) and linked lists for use in ML feature storage and minibatch construction. Describe performance for: random access, sequential iteration, insertions/deletions, memory overhead per element, CPU cache behavior and prefetch friendliness, and when you'd choose one over the other in practical ML pipelines (e.g., streaming features, online feature ingestion, or constructing GPU-ready batches). Include implications for latency-sensitive inference services.

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