13 Tough Data + AI Interview Questions from Top Companies – Can You Master the Latest Trends?

Q6. Is vector retrieval enough for RAG in a data warehouse?

The author states that relying solely on embedding‑plus‑cosine vector search is insufficient. In real‑world online scenarios, search systems have shifted from serving humans to feeding models, and models can only perceive the retrieved data. Pure vector retrieval cannot precisely hit exact keywords, proprietary names, IDs, or short conversational queries, leading to unstable recall and downstream errors.

Q7. How do metric platforms/semantic layers combine with AI to support “data for everyone”?

The ideal use case is a user asking a phone, “Show me yesterday’s DAU, retention, and sales.” The system responds quickly and accurately by treating metrics as prompts for a large language model (LLM). Metric definitions and metadata are fed to the LLM, which generates precise, executable SQL based on predefined business definitions, ensuring consistent terminology (the "Mandarin of data") while satisfying flexible user combinations.

Q8. What can large models do for data‑warehouse development? Example of “AI feeding back to the warehouse”.

Typical scenarios include data‑warehouse model review and metadata completion. Model review is labor‑intensive because implementations vary across engineers and require extensive context. The proposed method uses an LLM to extract structured features from warehouse models, combines them with a knowledge‑service (MCP), and builds a multi‑dimensional quantitative evaluation framework (reasonableness, compliance, redundancy, accuracy) to identify issues early, turning a "black‑box review" into a measurable assessment. For metadata completion, a "table‑description expert + field‑description expert" agent infers annotations from upstream/downstream lineage and field structures.

Q9. Why can’t RAG rely solely on vector search? How does BM25 + vector hybrid retrieval work and what are its advantages?

Pure vector search excels at semantic similarity but fails on exact keyword, proprietary term, or short query scenarios. In spoken‑query Top‑5 tests, pure vector recall is only about 60%. In data‑warehouse RAG, exact queries like field names, metric codes, and table names are frequent, and missed recalls cause missing context for the model.

Why vector retrieval is insufficient: it cannot precisely hit exact terms, leading to low recall for critical queries.

How to implement BM25 + vector hybrid retrieval.

Standard practice follows four steps:

Multi‑path parallel recall: one path uses BM25 (sparse/TF‑IDF) for exact keyword hits, the other uses dense vector search for semantic similarity; each returns its Top‑K results.

RRF fusion: Reciprocal Rank Fusion merges the two rankings without needing to compare raw scores, avoiding incompatibility between BM25 scores and cosine similarity.

Reranker refinement: A cross‑encoder (e.g., BGE‑Reranker) re‑ranks the fused candidate set, pushing the most relevant items to the top.

Parameter tuning: Data engineers adjust BM25 parameters, tokenizers, and indexing strategies to raise the recall ceiling.

Hybrid recall typically improves overall recall by 15–20% because BM25 fills the exact‑match gaps of vectors, while vectors complement BM25 with semantic generalization, and RRF combines their strengths without mutual degradation.

Q10. Designing a data‑agent foundation for the warehouse – key considerations

Unified storage + semantic awareness: Use open formats such as Iceberg or Paimon to ingest structured, semi‑structured, vector, and spatio‑temporal data, eliminating data silos.

Machine‑readable metadata (Context Layer): Elevate metadata from a simple database dictionary to an AI‑aware context graph that provides field semantics, table lineage, update frequency, and applicability.

AI‑generated code execution: Deploy code generated by AI close to the data to avoid large data movement overhead; agent operations run in isolated environments separate from production.

Agent‑aware governance: When agents drive the system, security, compliance, and permission policies must be agent‑aware, enabling pipelines to shift from manual configuration to self‑healing optimization.

That concludes the sharing.