pipelines

The unglamorous design question that exposes who has run LLMs in production. Batch APIs at 50% off, idempotency keys, poison records, spend kill-switches: the full checklist interviewers listen for.

Sounds like a definition question; it's actually a judgment question. The economics that flipped the industry to ELT, and the four cases (starting with PII) where transforming before load is still non-negotiable.

The pipeline-design question that exposes whether you've ever been paged. One property, idempotency, solves reruns, late data, and backfills at once, and there are exactly three patterns that deliver it.

Pure FDE territory: the customer's data is always dirtier than scoped. The layered defense, contracts, quarantine, drift handling, and the alert-severity matrix that keeps you from crying wolf.

The reported Databricks Solutions Architect live-coding staple. Working code is table stakes, the differentiators are explicit schemas, corrupt-record handling, idempotent re-runs, and knowing when explode_outer beats explode.

The aggregate-restatement problem that breaks naive backfills: events arrive days late and out of order, so any partition you already published may be wrong. The staff answer is event-time partitioning plus bounded restatement, not a bigger watermark.

The AWS ML Engineer staple. The answer that scores is a clean division of labor, CodePipeline owns code, SageMaker Pipelines owns the ML DAG, the Model Registry is the handoff point, plus the cross-account detail that separates real builds from doc reading.

Microsoft's scenario rounds reward one specific framing: two pipeline systems, two jobs, one handoff point. Which tool owns triggers and approvals, which owns compute and lineage, and the managed-endpoint detail that makes blue-green trivial.

'Retrain weekly' is the answer interviewers let you finish before dismantling it. The four trigger types, how to stop a drift alarm from causing a retraining storm, and why the trigger question is really a cost-and-staleness trade.

The experience probe where tool soup fails and one well-told pipeline wins. The context-stack-hardest-problem-outcome structure, the two challenges every real implementation hits, and the before/after numbers that make the story land.