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

80 practiced

Design a data layout and access strategy to maximize throughput for mixed-precision sparse-dense hybrid layers on GPU. Specify data structures that represent the sparse indices and dense parameters, memory alignment and packing strategy, conversion costs between formats, and how to schedule kernel execution to overlap memory transfers and computation.

MediumTechnical

76 practiced

Design an efficient in-memory representation for very high-dimensional sparse categorical features used in embedding tables (billions of possible categories but most zero per example). Discuss trade-offs between storing indices as 32-bit vs 64-bit, using packed index blocks, and dictionary-compressed formats for fast lookup during training/inference.

MediumTechnical

80 practiced

You are asked to maintain a compact summary for each user's recent activity (e.g., last-24-hour features) with frequent writes and reads. Compare per-user ring buffers vs centralized time-partitioned logs plus an index. Discuss update/read complexity, memory footprint, and how to support TTL-based automatic pruning efficiently.

HardTechnical

70 practiced

Propose a design for a lock-free concurrent hash table optimized for storing small metadata entries pinned on CPU and queried by GPU-hosted compute kernels via unified memory. Discuss memory model assumptions, atomic operations used, strategies for resizing/rehashing without global locks, and how to avoid ABA problems on modern CPUs/GPUs.

EasyTechnical

75 practiced

You need a lookup for model metadata keyed by string identifiers in a latency-sensitive inference service. Compare using a hash map (hash table) versus a balanced search tree (e.g., red-black tree) for this use case. Discuss average/worst-case access times, memory overhead, iteration order, concurrency implications, and how resizing/rehashing affects tail latency under heavy load.

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