Databricks AI GenAI Engineer Interview Questions

Scenario question on offline golden eval vs online A/B, feedback, and guardrail metrics in production.

Hallucination in RAG systems is the #1 production failure mode cited in AI engineering interviews. Your interviewer wants a systematic debugging framework — not a list of buzzwords. Learn how to measure faithfulness, fix retrieval precision, and layer mitigations the way senior engineers at Databricks and Meta actually ship RAG.

Hard RAG ops question on index versioning, dual-write, and blue-green cutovers.

**Pipeline** Pre-filter user input → model → post-filter output before display; async moderation for streaming with revoke.

Hard RAG interview question on databricks interview: lakehouse rag — architecture, trade-offs, eval, and production patterns.

Scenario question on classical NLP metrics — what they measure, where they break on paraphrase and factuality, and what to use instead.

Scenario question comparing prompting, RAG, and fine-tuning for domain knowledge — freshness, cost, auditability, and when to combine approaches.