clustering
Applied AI interview questions tagged clustering, across every topic.
5 questions · 1 unlocked for you
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). Applied-AI interviews probe it to see whether you can pick the right algorithm for 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 assigns 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 exposes 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 instead of the whole table. Clustering and sort keys order data within storage so related rows sit together, improving locality and letting the engine skip blocks. Applied-AI interviews probe this because in a cloud warehouse you pay per byte scanned, and turning a full scan into a thin slice is the difference between a query that costs cents and one that costs dollars and minutes.🗄️ Data & SQL EngineeringSign in