Interview questions in Data Science & Analytics, page 6
Batch prediction versus online real-time prediction
Batch is scheduled bulk scoring, online is low-latency per-request scoring; contrast latency, freshness, cost; give a use case each.
Detecting and responding to model and concept drift
Define drift, pick a metric like PSI or falling AUC against labels, then investigate, retrain, validate.
What a feature store solves: skew and consistency
Central repository of computed features, one definition serving training and inference, reuse across models.
ML CI/CD versus traditional software CI/CD
Validates code plus data plus the model, auto-trains and evaluates, adds continuous training and monitoring.
CPU versus GPU serving: cost, latency, throughput
GPUs win on throughput for batched parallel work but cost more; CPUs suit low-volume or small models.
Why version data and model artifacts, not just code
Code alone cannot reproduce a model; data and artifact versioning enable rollback, debugging, audit.
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.
Programmatically detect and redact PII in text
Regex for structured PII plus NER for names and places, redact or tokenize, then validate recall.
Quantifying performance disparity across subgroups
Compute per-group precision, recall, FPR, FNR, compare via fairness metrics; visualize with grouped bars or per-group confusion matrices.
Right to be forgotten and machine unlearning
Delete raw data everywhere, then remove influence via full retraining, SISA sharded retraining, or approximate unlearning; note cost and verification.
k-anonymity and its limits against linkage attacks
Define k-anonymity via indistinguishable quasi-identifier groups, apply generalization and suppression, then show homogeneity or linkage attacks break it.
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.
Risks of optimizing recommendations only for CTR
CTR-only invites clickbait, low satisfaction, and long-term churn; add counter-metrics like dwell time, satisfaction, retention, and diversity.
pandas .apply() versus vectorized operations
Apply runs a Python function per row or column, flexible but slow due to per-element looping; prefer vectorized ops; use apply only for custom logic with no vectorized equivalent.
When should you keep outliers, not drop them?
Keep them in fraud or anomaly detection, use robust models and metrics.
Explain KNN or MICE imputation principles
KNN borrows from similar rows, MICE models each variable from the others iteratively and creates multiple datasets.
Design a SQL upsert from a staging table
Define a stable key, use MERGE or INSERT ON CONFLICT, dedupe the staging set first, run in a transaction.
Handle source schema changes without downtime
Add columns as nullable additive changes, version the schema, use formats like Iceberg or Parquet that support evolution, backfill new types safely.
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