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.
HardSystem Design
0 practiced
Design a system that allows dynamic insertion and deletion of vectors in an ANN index while maintaining low-latency queries. Discuss strategies such as background rebuilding, lazy deletion (tombstones), incremental graph maintenance for HNSW, and how to bound degradation in recall and latency over time.
EasyTechnical
0 practiced
Compare sparse matrix formats (CSR, CSC, COO) versus dense ndarrays for ML workloads. For each format, describe memory layout, efficient operations (mat-vec, mat-mat), typical use-cases (e.g., sparse features, graph adjacency), and how conversion costs and GPU support affect your decision for training and inference.
MediumSystem Design
0 practiced
For a 2D multiplayer game server that must do many proximity queries each tick, evaluate quadtree vs spatial hashing vs kd-tree for indexing moving player positions. Discuss update costs, query performance for radius searches, memory overhead, ease of parallelization, and stability when objects move frequently across partitions.
HardSystem Design
0 practiced
Design an on-disk inverted index for textual features used in a retrieval/feature extraction pipeline. Describe postings list representation, compression (e.g., delta-encoding, variable-byte, PForDelta), support for prefix queries, merging updates efficiently, and trade-offs between memory-mapped files and explicit IO and caching.
EasyTechnical
0 practiced
Explain trade-offs between using a hash map (hash table) and an ordered tree map (e.g., red-black or AVL) for feature lookup in an online inference service. Discuss average-case and worst-case time complexity, memory overhead, iteration order guarantees, concurrency considerations (lock granularity), and scenarios where order or range queries matter (e.g., range-based feature aggregation).
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