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
79 practiced
Design a robust training pipeline that tolerates up to 20% noisy labels. Consider robust loss functions, co-teaching, sample selection, label cleansing, and human-in-the-loop verification. Explain detection heuristics to identify noisy labels and how to avoid removing hard-but-correct samples.
HardSystem Design
93 practiced
Design a federated learning system to personalize a language model on-device for millions of users with privacy constraints. Explain algorithmic choices for client update aggregation (FedAvg vs FedProx), compression of updates (quantization, sparsification), handling non-iid client data, and whether to use synchronous or asynchronous aggregation under intermittent connectivity.
MediumTechnical
81 practiced
For large-scale deduplication of text documents, explain when you would choose Bloom filters, MinHash + LSH, or exact hashing. Describe trade-offs in false positives/negatives, memory, ingestion throughput, and search complexity for scales such as 1M, 100M and 1B documents.
HardSystem Design
130 practiced
Design a multi-tenant model serving architecture to serve dozens of large transformer models (~10B parameters each) with p99 latency < 100ms. Describe algorithmic choices for request routing, batching strategies, quantization/pruning decisions, model partitioning (model-parallel vs pipeline-parallel), caching of hot prompts, and how workload patterns influence these choices.
HardTechnical
78 practiced
Your production recommendation model shows degradation over weeks. Describe how you would recognize different kinds of drift (covariate shift, label shift, concept drift), the algorithms or tests to detect each (e.g., population stability index, KS-test, monitoring label-distribution vs feature-distribution), and algorithmic responses (retraining frequency, online adaptation, model family change). Include rollback and alerting strategies.
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