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Microsoft Interview Problems (43)

Problems reported from Microsoft data science interviews

Status Title Difficulty
Outlier Detection with IQR Pro Medium
POS Tagging Purpose Pro Medium
Quantization for LLM Inference Pro Medium
Queue Using Two Stacks Pro Medium
Receiving Critical Feedback Pro Medium
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Rotate Matrix 90 Degrees Pro Medium
String Compression Pro Medium
Working with Engineering Teams Pro Medium
BLEU and ROUGE Metrics Pro Hard
Dependency Parsing Pro Hard
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Edit Distance Applications Pro Hard
Hinge Loss for SVMs vs Neural Networks Pro Hard
Kernel Density Estimation Pro Hard
KNN Curse of Dimensionality Pro Hard
KV Cache in Autoregressive Generation Pro Hard
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Model Serving: TorchServe and TF Serving Pro Hard
P vs NP Problem Pro Hard
Spiral Matrix Traversal Pro Hard
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