K Means Clustering and Unsupervised Learning Questions
Unsupervised learning finding patterns without labels. K-Means: algorithm for partitioning data into k clusters by iteratively assigning points to nearest cluster center and updating centers. For junior level, explain the algorithm steps, how to choose k (elbow method), distance metrics (Euclidean, Manhattan), advantages (simple, fast) and disadvantages (sensitive to initialization, assumes spherical clusters). Understand other approaches like hierarchical clustering and DBSCAN conceptually.
MediumSystem Design
0 practiced
Design an end-to-end pipeline to cluster 100 million text embeddings of dimension 768 into meaningful segments. Explain choices for dimensionality reduction, approximate nearest neighbor indexes (e.g., Faiss), mini-batch or approximate K Means variants, hardware choices (GPU vs CPU), storage, monitoring, and retraining strategy for new embeddings.
EasyTechnical
0 practiced
Explain the curse of dimensionality and how it affects distance-based clustering like K Means. Describe when and how you would apply PCA or training an autoencoder before clustering, and enumerate tradeoffs between linear and nonlinear dimensionality reduction.
MediumTechnical
0 practiced
Outline and implement an efficient approach to compute silhouette scores for a very large dataset by using sampling or approximate nearest neighbors. Provide pseudocode for the estimator and justify the sampling strategy and expected error bounds or confidence intervals.
HardSystem Design
0 practiced
Design a production system to run K Means clustering on a high-volume stream of events arriving at 100k events per second. Describe how you would maintain online cluster centers, detect and respond to concept drift, provide low-latency assignments for incoming events, and ensure state recovery and fault tolerance.
HardTechnical
0 practiced
Explain the spectral gap and why it matters in spectral clustering. Relate the eigenvalues of the graph Laplacian to clusterability, explain how a large gap between the k-th and k+1-th eigenvalues indicates well-separated clusters, and discuss how noise affects this interpretation.
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