Advanced interview questions in Data Science & Analytics, page 2
K-Means vs DBSCAN: which for geospatial hotspots?
Tests matching algorithmic assumptions to data structure. K-Means needs K and assumes spheres; DBSCAN discovers arbitrary density shapes and labels noise. Choose DBSCAN for geospatial hotspots because density is irregular.
Interpreting a black-box gradient boosting model
Global tools like permutation importance or aggregated SHAP rank overall feature influence; local tools like per-instance SHAP or LIME explain one prediction; SHAP unifies both via additive…
How would you estimate causal impact using a quasi-experimental method?
DiD with Canada versus Australia; assert parallel trends; validate with pre-period plots and placebo tests.
Explain Regression Discontinuity Design and propose a real-world scenario
Compare units just above and below a threshold for local effects; propose scenario with forcing variable.
Exploration versus exploitation: epsilon-greedy and UCB
Exploit current best to earn reward, explore to discover better options; epsilon-greedy explores randomly with probability epsilon; UCB explores by an uncertainty bonus…
Why Transformers use multi-head attention
A single head averages into one representation subspace; multiple heads attend in parallel to different subspaces, letting the model capture diverse relations like syntax and coreference at once, then…
Mode collapse in GANs and how to fix it
Mode collapse is the generator producing few similar outputs, missing data diversity to fool the discriminator; fixes include Wasserstein loss, minibatch discrimination, unrolled GANs, and feature…
Diagnosing Spark executor OutOfMemoryError
Check the Spark UI for skew and spills, inspect executor memory and partition count, find culprits like wide collect, huge shuffles, or skewed keys, and fix via more partitions, memory tuning, or…
Zero-downtime model updates with blue-green or canary
Blue-green swaps full traffic after validation, canary ramps a small slice; both need health, latency, and quality monitoring plus instant rollback.
Minimizing model prediction latency end to end
Shrink the model via quantization, pruning, distillation, compilation; cut infra latency via caching, edge, faster runtimes.
Differential privacy, epsilon, and noisy aggregates
Define DP as bounded output change when one record is added or removed, explain epsilon as the privacy-accuracy knob, add calibrated noise scaled to sensitivity.
Federated learning architecture, risks, and defenses
Devices train locally and send updates not data, a server aggregates; gradients still leak, enabling inversion or membership inference; defend with secure aggregation and DP.
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