Recognizing Patterns and Selecting Algorithms Questions

Ability to recognize problem patterns and know which algorithm/data structure is appropriate. Includes pattern matching like 'this looks like a sliding window problem' or 'this is a backtracking problem'.

HardTechnical

74 practiced

For learning-to-rank problems, explain when to use pointwise, pairwise, or listwise losses. Map data characteristics and evaluation metrics (NDCG, MAP) to the choice of loss and discuss computational complexity of each choice.

MediumTechnical

91 practiced

Concept drift: you observe model performance degrades over time. List detection and remediation algorithmic patterns (drift detectors like ADWIN, sliding-window retraining, online learning, ensemble weighting by recency, validation via holdout streams) and discuss when to prefer each.

MediumTechnical

94 practiced

High-dimensional dense vectors (d=1024) need nearest-neighbor search. Under what dataset sizes and QPS conditions is a brute-force linear scan acceptable versus requiring an index like HNSW or IVF+PQ? How do dimensionality reduction techniques (PCA, random projection) or quantization change that decision?

HardTechnical

71 practiced

Production fairness issue: model shows disparate performance across groups. Propose algorithmic and data approaches to detect, attribute, and mitigate bias (fairness metrics, reweighting, constrained optimization, adversarial debiasing, post-processing). Discuss evaluation and trade-offs with accuracy and utility.

MediumTechnical

97 practiced

Compare batch gradient descent, mini-batch SGD, and second-order methods (LBFGS/Newton) as algorithmic patterns for different ML problems. Discuss when noise from mini-batches helps generalization and when second-order methods are beneficial.

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