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Advanced batch vs streaming design: exactly-once semantics, stream-table duality, dual pipelines, state scaling, cost/latency tradeoffs, and operational failure modes. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Choosing and operating batch vs streaming paths: windows, watermarks, delivery semantics, backpressure, CDC, and serving fresh features. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Foundations of batch and real-time data processing: latency, scheduling, use cases, and how pipelines differ. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Advanced data drift for production ML: multivariate and embedding drift, MMD and domain classifiers, concept vs covariate shift under delay, causal pitfalls, and governance of retraining. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Operational data drift detection: PSI and statistical tests, feature vs prediction drift, windowing, alert design, and retraining triggers. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Foundations of data drift in ML: covariate shift, monitoring live inputs against a reference baseline, and why production distributions change. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Advanced model registry governance: multi-region replication, model cards, supply-chain attestations, shadow/canary patterns, federated registration, and enterprise audit design. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Operational model registry practice: aliases and champions, promotion gates, signatures, access control, CI/CD handoffs, and comparing registered versions before production. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
Foundations of ML model registries: versioning, stages, artifacts, lineage, and why teams centralize models instead of scattering files. Mix of single-answer and multiple-answer items; multi-select questions ask you to select every correct statement.
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