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.

EasyTechnical

0 practiced

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.

EasyTechnical

0 practiced

You're deciding between serializing model metadata as JSON strings and using compact byte arrays (e.g., Protocol Buffers) for a high-throughput telemetry pipeline. Discuss serialization/deserialization CPU cost, network bandwidth, backward/forward compatibility, ease of debugging, and how choice affects CPU cache behavior and memory fragmentation in long-running services.

HardSystem Design

0 practiced

Design a disk-backed, lock-free key-value store optimized for large sparse embeddings where reads dominate writes. Choose between LSM-tree and B-tree layouts, explain compaction and write-amplification trade-offs, caching strategies for hot keys, and how to provide amortized low-latency reads while keeping updates efficient.

MediumTechnical

0 practiced

You have a sharded search index where each shard returns its top-k local results. Describe algorithms and data structures to merge per-shard top-k lists efficiently into a global top-k response with minimal latency (consider millions of documents), and discuss how to handle tie-breaking and score normalization across shards.

MediumTechnical

0 practiced

Compare CSR (Compressed Sparse Row) and COO (Coordinate) formats for representing sparse input matrices fed into GPU-accelerated models. Discuss conversion cost, memory layout benefits when transferring to GPU, batching trade-offs, and which formats deep learning libraries (PyTorch/TensorFlow) prefer for sparse ops.

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