Everything in AI & ML, page 7
Design an automated A/B test reporting system
Standardized metric definitions, automated stats with confidence intervals and guardrails, segment breakdowns, a clear ship recommendation.
Explain false positives and negatives for churn
False positive flags a loyal customer (wasted incentive), false negative misses a leaver (lost customer), tie to threshold choice.
Catalyst Optimizer and Project Tungsten in Spark
Catalyst transforms logical plans with rules, picks physical plans by cost; Tungsten optimizes execution with off-heap memory and codegen.
Primary metric up, guardrail down: ship or not?
Tie metrics to business value, weigh short-term lift against retention damage, use guardrails and an overall evaluation criterion.
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.
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.
Explain KNN or MICE imputation principles
KNN borrows from similar rows, MICE models each variable from the others iteratively and creates multiple datasets.
When should you keep outliers, not drop them?
Keep them in fraud or anomaly detection, use robust models and metrics.
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.
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.
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.
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.
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.
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.
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.
Programmatically detect and redact PII in text
Regex for structured PII plus NER for names and places, redact or tokenize, then validate recall.
Minimizing model prediction latency end to end
Shrink the model via quantization, pruning, distillation, compilation; cut infra latency via caching, edge, faster runtimes.
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.
Why version data and model artifacts, not just code
Code alone cannot reproduce a model; data and artifact versioning enable rollback, debugging, audit.
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.
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