Microsoft·Data Scientist·Technical Phone Screen
Jul 2026
Microsoft Data Scientist interview focused heavily on KNN fundamentals, which sounds basic until you're actually in the room trying to explain curse of dimensionality without sounding like you memorized a textbook. Pretty technical throughout, no behavioral fluff.
- Walk me through how KNN works for both classification and regression tasks.
- What are the main hyperparameters in KNN and how do they affect model behavior? Things like K, distance metric, and neighbor weighting.
- What preprocessing steps matter most for KNN and why?
- What are KNN's biggest weaknesses, and how does it behave under class imbalance, high dimensionality, or large datasets?
- How would you choose K and evaluate the model in a way that prevents overfitting?
- How could dimensionality reduction like PCA help KNN, and are there cases where it would actually hurt?
“Started fine on classification, majority vote among neighbors, clean enough.” The rest of the author's notes on Data Scientist interview at Microsoft, Technical Phone Screen round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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