clustering
AI, ML & GenAI interview questions tagged clustering, across every topic.
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Concepts behind "clustering"
The curriculum that explains the ideas these questions test.
Foundational
Clustering: K-Means, Hierarchical, DBSCANClustering groups unlabeled points by similarity. The three workhorses are k-means (fast, assumes round blobs, you pick k), agglomerative hierarchical (builds a dendrogram, no fixed k upfront), and DBSCAN (density-based, finds arbitrary shapes and flags noise). AI, ML, and GenAI engineer interviews probe it to see whether you can match the right algorithm to the data geometry and actually validate clusters rather than trusting a pretty plot.📊 Evaluation & ML Foundations
Core
Gaussian Mixtures and the EM AlgorithmA Gaussian mixture model treats data as generated by several Gaussian components and gives each point a soft, probabilistic membership rather than a hard cluster label. Expectation-maximization fits it by alternating between computing those memberships and re-estimating each component. Interviews probe it because it is the cleanest example of a latent-variable model and reveals whether a candidate understands soft clustering, local optima, and how GMM generalizes k-means.📊 Evaluation & ML FoundationsSign in
Core
Partitioning and ClusteringPartitioning splits one large table into physically separate chunks by a key (usually date), so a query with a matching filter reads only the relevant partitions rather than the whole table. Clustering and sort keys order data within storage so related rows sit together, improving locality and letting the engine skip blocks. AI, ML, and GenAI interviews probe this because in a cloud warehouse you pay per byte scanned, and turning a full scan into a thin slice separates a query that costs cents from one that costs dollars and minutes.🗄️ Data & SQL EngineeringSign in